Big Black Friday Sale 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: exams65

ExamsBrite Dumps

AWS Certified Data Engineer - Associate (DEA-C01) Question and Answers

AWS Certified Data Engineer - Associate (DEA-C01)

Last Update Nov 30, 2025
Total Questions : 218

We are offering FREE Data-Engineer-Associate Amazon Web Services exam questions. All you do is to just go and sign up. Give your details, prepare Data-Engineer-Associate free exam questions and then go for complete pool of AWS Certified Data Engineer - Associate (DEA-C01) test questions that will help you more.

Data-Engineer-Associate pdf

Data-Engineer-Associate PDF

$36.75  $104.99
Data-Engineer-Associate Engine

Data-Engineer-Associate Testing Engine

$43.75  $124.99
Data-Engineer-Associate PDF + Engine

Data-Engineer-Associate PDF + Testing Engine

$57.75  $164.99
Questions 1

A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Establish WebSocket connections to Amazon Redshift.

B.  

Use the Amazon Redshift Data API.

C.  

Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.

D.  

Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

Discussion 0
Questions 2

A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoDB.

The company needs the workflow to perform specific steps based on the content of the incoming data.

Which Step Functions state type should the company use to meet this requirement?

Options:

A.  

Parallel

B.  

Choice

C.  

Task

D.  

Map

Discussion 0
Questions 3

A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management. Which solution will meet these requirements?

Options:

A.  

Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AW5 Glue for each day's data.

B.  

Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.

C.  

Reduce the number of partitions by changing the partitioning schema from dairy to monthly granularity.

D.  

Increase the processing capacity of Athena queries by allocating more compute resources.

Discussion 0
Questions 4

A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.

The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

AWS Glue workflows

B.  

AWS Step Functions tasks

C.  

AWS Lambda functions

D.  

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

Discussion 0
Questions 5

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.

The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.

Which rule will meet these requirements?

Options:

A.  

DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B.  

ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C.  

DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D.  

ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Discussion 0
Questions 6

A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.

The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.

Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

Options:

A.  

Turn on the public access setting for the DB instance.

B.  

Update the security group of the DB instance to allow only Lambda function invocations on the database port.

C.  

Configure the Lambda function to run in the same subnet that the DB instance uses.

D.  

Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.

E.  

Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

Discussion 0
Questions 7

A data engineer needs to run a data transformation job whenever a user adds a file to an Amazon S3 bucket. The job will run for less than 1 minute. The job must send the output through an email message to the data engineer. The data engineer expects users to add one file every hour of the day.

Which solution will meet these requirements in the MOST operationally efficient way?

Options:

A.  

Create a small Amazon EC2 instance that polls the S3 bucket for new files. Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.

B.  

Run an Amazon Elastic Container Service (Amazon ECS) task to poll the S3 bucket for new files. Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.

C.  

Create an AWS Lambda function to transform the data. Use Amazon S3 Event Notifications to invoke the Lambda function when a new object is created. Publish the output to an Amazon Simple Notification Service (Amazon SNS) topic. Subscribe the data engineer's email account to the topic.

D.  

Deploy an Amazon EMR cluster. Use EMR File System (EMRFS) to access the files in the S3 bucket. Run transformation code on a schedule to generate the output to a second S3 bucket. Create an Amazon Simple Notification Service (Amazon SNS) topic. Configure Amazon S3 Event Notifications to notify the topic when a new object is created.

Discussion 0
Questions 8

A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.

The company needs to optimize the data format for analytical queries.

Which solutions will meet these requirements with the SHORTEST query times? (Select TWO.)

Options:

A.  

Use Avro format. Use AWS Glue Data Catalog to track schema changes.

B.  

Use ORC format. Use AWS Glue Data Catalog to track schema changes.

C.  

Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.

D.  

Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.

E.  

Use ORC format. Store schema definitions in separate files in Amazon S3.

Discussion 0
Questions 9

A company needs to implement a new inventory management system that provides near real-time updates and visibility across all AWS Regions. The new solution must provide centralized access control over data access and permissions. The company has a separate inventory management team assigned to each Region. Each inventory management team needs to update inventory levels.

A data engineer must implement Amazon Redshift data sharing with write capabilities. The solution must follow the principle of least privilege.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Configure a single Redshift datashare from the company's headquarters that provides read-only access for all Regions. Configure a separate AWS Glue ETL job to update data for each Region.

B.  

Configure three Regional Redshift datashares that provide full write access. Allow full self-managed access controls.

C.  

Configure a single Redshift datashare from the company's headquarters that has selective write permissions for inventory. Set up Regional namespace controls.

D.  

Configure separate Redshift datashares for multiple table types that provide full write access. Distribute the datashares across all Regional clusters. Allow self-managed Regional schema permissions.

Discussion 0
Questions 10

A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.

The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.

Which AWS service should the company use to meet these requirements?

Options:

A.  

AWS Lambda

B.  

AWS Database Migration Service (AWS DMS)

C.  

AWS Direct Connect

D.  

AWS DataSync

Discussion 0
Questions 11

A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned. An AWS Glue crawler updates the partitions.

The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.

Which solution will meet these requirements?

Options:

A.  

Apply partition filters in the queries.

B.  

Increase the frequency of AWS Glue crawler invocations to update the data catalog more often.

C.  

Organize the data that is in Amazon S3 by using a nested directory structure.

D.  

Configure Spark to use in-memory caching for frequently accessed data.

Discussion 0
Questions 12

A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift.

The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs. However, the deployed Step Functions state machine is not able to run the EMR jobs.

Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)

Options:

A.  

Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.

B.  

Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.

C.  

Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).

D.  

Query the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.

E.  

Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.

Discussion 0
Questions 13

A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.  

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.

B.  

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.

C.  

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.

D.  

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.

Discussion 0
Questions 14

A company needs to implement a data mesh architecture for trading, risk, and compliance teams. Each team has its own data but needs to share views. They have 1,000+ tables in 50 Glue databases. All teams use Athena and Redshift, and compliance requires full auditing and PII access control.

Options:

A.  

Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.

B.  

Use AWS Glue Data Catalog views. Use CloudTrail logs and Lake Formation to manage permissions.

C.  

Use Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.

D.  

Create materialized views and enable Amazon Redshift datashares for each domain.

Discussion 0
Questions 15

A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.

The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.

Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)

Options:

A.  

Create views of the tables that need to be shared. Include only the required columns.

B.  

Create an Amazon Redshift data than that includes the tables that need to be shared.

C.  

Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.

D.  

Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.

E.  

Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.

Discussion 0
Questions 16

A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.

Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.

Which combination of solutions will meet these requirements? (Select TWO.)

Options:

A.  

Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.

B.  

Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.

C.  

Configure an Amazon Made discovery job for the S3 bucket.

D.  

Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.

E.  

Write custom scripts in an application to mask the PII data and to control access.

Discussion 0
Questions 17

A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.

Which Step Functions state should the data engineer use to meet these requirements?

Options:

A.  

Parallel state

B.  

Choice state

C.  

Map state

D.  

Wait state

Discussion 0
Questions 18

A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old-table.

Which SQL statement should the data engineer use to meet this requirement?

Options:

A.  

B.  

C.  

D.  

Discussion 0
Questions 19

A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.

The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.

Which solution will meet these requirements with the LOWEST latency?

Options:

A.  

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

B.  

Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.

C.  

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.

D.  

Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

Discussion 0
Questions 20

A company stores sales data in an Amazon RDS for MySQL database. The company needs to start a reporting process between 6:00 A.M. and 6:10 A.M. every Monday. The reporting process must generate a CSV file and store the file in an Amazon S3 bucket.

Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

Options:

A.  

Create an Amazon EventBridge rule to run every Monday at 6:00

A.  

M.

B.  

Create an Amazon EventBridge Scheduler to run every Monday at 6:00 A.M.

C.  

Create and invoke an AWS Batch job that runs a script in an Amazon Elastic Container Service (Amazon ECS) container. Configure the script to generate the report and to save it to the S3 bucket.

D.  

Create and invoke an AWS Glue ETL job to generate the report and to save it to the S3 bucket.

E.  

Create and invoke an Amazon EMR Serverless job to generate the report and to save it to the S3 bucket.

Discussion 0
Questions 21

A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.

B.  

Create an AWS Lambda Python function with provisioned concurrency.

C.  

Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).

D.  

Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.

Discussion 0
Questions 22

A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark Jobs that use the Iceberg framework.

What combination of steps will meet these requirements? (Select TWO.)

Options:

A.  

Create a key named -conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.

B.  

Specify the path to a specific version of Iceberg by using the --extra-Jars job parameter. Set Iceberg as a value for the ~ datalake-formats job parameter.

C.  

Set Iceberg as a value for the -datalake-formats job parameter.

D.  

Set the -enable-auto-scaling parameter to true.

E.  

Add the -job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.

Discussion 0
Questions 23

A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.

The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.

Which solution will MOST reduce the data processing time?

Options:

A.  

Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B.  

Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C.  

Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D.  

Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Discussion 0
Questions 24

A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.

The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.

B.  

Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.

C.  

Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.

D.  

Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Discussion 0
Questions 25

A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.

A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.

Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

Options:

A.  

Use data filters for each Region to register the S3 paths as data locations.

B.  

Register the S3 path as an AWS Lake Formation location.

C.  

Modify the IAM roles of the HR departments to add a data filter for each department's Region.

D.  

Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.

E.  

Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.

Discussion 0
Questions 26

A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between 100 KB and 300 KB.

A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command. Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger.

B.  

Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.

C.  

Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.

D.  

Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.

Discussion 0
Questions 27

A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.

The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets. Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.

B.  

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.

C.  

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.

D.  

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.

Discussion 0
Questions 28

A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.

A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.

Which solution will meet this requirement?

Options:

A.  

Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.

B.  

Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.

C.  

Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.

D.  

Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.

Discussion 0
Questions 29

A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.

A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.

B.  

Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialIntegrity rule for each join.

C.  

Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested columns contain null values. Use a second SQL transform to check referential integrity.

D.  

Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.

Discussion 0
Questions 30

A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.

The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.  

Use AWS Glue Python jobs to read and transform the CSV files.

B.  

Use an AWS Glue custom crawler to read and transform the CSV files.

C.  

Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.

D.  

Use AWS Glue DataBrew recipes to read and transform the CSV files.

Discussion 0
Questions 31

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.

The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.

The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.

Which solution will meet these requirements?

Options:

A.  

Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.

B.  

Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

C.  

Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

D.  

Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

Discussion 0
Questions 32

A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.

A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.

How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

Options:

A.  

Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.

B.  

Use the Amazon Redshift Data API to publish an event to Amazon EventBridqe. Configure an EventBridge rule to invoke the Lambda function.

C.  

Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.

D.  

Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

Discussion 0
Questions 33

A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.

The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.

Which solution will meet these requirements?

Options:

A.  

Create an AWS Glue DataBrew project to profile the sensor data. Define completeness rules for timestamps. Set up numeric range validation for temperature values.

B.  

Use AWS Glue's Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.

C.  

Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.

D.  

Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.

Discussion 0
Questions 34

A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.

The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.

Which Amazon Redshift command will meet these requirements?

Options:

A.  

VACUUM FULL Orders

B.  

VACUUM DELETE ONLY Orders

C.  

VACUUM REINDEX Orders

D.  

VACUUM SORT ONLY Orders

Discussion 0
Questions 35

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.

The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.

Which service will meet these requirements?

Options:

A.  

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

B.  

AWS Step Functions

C.  

AWS Glue

D.  

Amazon EventBridge

Discussion 0
Questions 36

A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.

A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).

Which solution will meet these requirements?

Options:

A.  

Define and create the calculated field in the dataset.

B.  

Define and create the calculated field in the analysis.

C.  

Define and create the calculated field in the visual.

D.  

Define and create the calculated field in the dashboard.

Discussion 0
Questions 37

A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.

A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.

B.  

Use the streaming ingestion feature of Amazon Redshift.

C.  

Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.

D.  

Use the Amazon Aurora zero-ETL integration with Amazon Redshift.

Discussion 0
Questions 38

The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.

The company needs to cost-optimize its Amazon S3 storage.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.  

Apply a lifecycle policy to transition records to S3 Standard Infrequent-Access (S3 Standard-IA) storage after 30 days.

B.  

Use S3 Intelligent-Tiering storage.

C.  

Transition records to S3 Glacier Deep Archive storage after 30 days.

D.  

Use S3 Standard-Infrequent Access (S3 Standard-IA) storage for all customer records.

Discussion 0
Questions 39

A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.

The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.

Which solution will meet these requirements?

Options:

A.  

Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.

B.  

Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.

C.  

Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.

D.  

Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.

Discussion 0
Questions 40

A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.

Which solution will meet these requirements with the LEAST ongoing maintenance?

Options:

A.  

Use the DynamoDB TTL feature to automatically expire data based on timestamps.

B.  

Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.

C.  

Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.

D.  

Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than 1 month. Configure the Lambda function to delete old data.

Discussion 0
Questions 41

A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.  

Use Amazon EMR and Apache Ranger.

B.  

Use a Hive metastore on an EMR cluster.

C.  

Use the AWS Glue Data Catalog.

D.  

Use a metastore on an Amazon RDS for MySQL DB instance.

Discussion 0
Questions 42

A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.

The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.

The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.

Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

Options:

A.  

Configure the third-party application to create the files in a columnar format.

B.  

Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.

C.  

Partition the order data in the S3 bucket based on order date.

D.  

Configure the third-party application to create the files in JSON format.

E.  

Load the JSON data into the Amazon Redshift table in a SUPER type column.

Discussion 0
Questions 43

A company uses Amazon Redshift as its data warehouse service. A data engineer needs to design a physical data model.

The data engineer encounters a de-normalized table that is growing in size. The table does not have a suitable column to use as the distribution key.

Which distribution style should the data engineer use to meet these requirements with the LEAST maintenance overhead?

Options:

A.  

ALL distribution

B.  

EVEN distribution

C.  

AUTO distribution

D.  

KEY distribution

Discussion 0
Questions 44

A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends.

The company must ensure that the application performs consistently during peak usage times.

Which solution will meet these requirements in the MOST cost-effective way?

Options:

A.  

Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.

B.  

Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.

C.  

Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.

D.  

Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

Discussion 0
Questions 45

A data engineer is configuring Amazon SageMaker Studio to use AWS Glue interactive sessions to prepare data for machine learning (ML) models.

The data engineer receives an access denied error when the data engineer tries to prepare the data by using SageMaker Studio.

Which change should the engineer make to gain access to SageMaker Studio?

Options:

A.  

Add the AWSGlueServiceRole managed policy to the data engineer's IAM user.

B.  

Add a policy to the data engineer's IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy.

C.  

Add the AmazonSageMakerFullAccess managed policy to the data engineer's IAM user.

D.  

Add a policy to the data engineer's IAM user that allows the sts:AddAssociation action for the AWS Glue and SageMaker service principals in the trust policy.

Discussion 0
Questions 46

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.

Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Options:

A.  

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B.  

Amazon AppFlow

C.  

AWS Glue Data Catalog

D.  

Amazon Kinesis

Discussion 0
Questions 47

A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.

Which data pipeline solutions will meet these requirements? (Choose two.)

Options:

A.  

Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

B.  

Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

C.  

Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.

D.  

Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

E.  

Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.

Discussion 0
Questions 48

A company wants to ingest streaming data into an Amazon Redshift data warehouse from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A data engineer needs to develop a solution that provides low data access time and that optimizes storage costs.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Create an external schema that maps to the MSK cluster. Create a materialized view that references the external schema to consume the streaming data from the MSK topic.

B.  

Develop an AWS Glue streaming extract, transform, and load (ETL) job to process the incoming data from Amazon MSK. Load the data into Amazon S3. Use Amazon Redshift Spectrum to read the data from Amazon S3.

C.  

Create an external schema that maps to the streaming data source. Create a new Amazon Redshift table that references the external schema.

D.  

Create an Amazon S3 bucket. Ingest the data from Amazon MSK. Create an event-driven AWS Lambda function to load the data from the S3 bucket to a new Amazon Redshift table.

Discussion 0
Questions 49

A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.

Which solution will meet these requirements with the LEAST effort?

Options:

A.  

Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.

B.  

Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.

C.  

Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.

D.  

Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.

Discussion 0
Questions 50

A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the file and load the data into Amazon Redshift. The company wants to process the file as quickly as possible.

Which solution will meet these requirements?

Options:

A.  

Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Lambda function that runs every 15 minutes to check the S3 bucket for the daily file. Configure the function to start the AWS Glue workflow if the file is present.

B.  

Create an event-based AWS Glue trigger to start the workflow. Configure Amazon S3 to log events to AWS CloudTrail. Create a rule in Amazon EventBridge to forward PutObject events to the AWS Glue trigger.

C.  

Create a scheduled AWS Glue trigger to start the workflow. Create a cron job that runs the AWS Glue job every 15 minutes. Set up the AWS Glue job to check the S3 bucket for the daily file. Configure the job to stop if the file is not present.

D.  

Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Database Migration Service (AWS DMS) migration task. Set the DMS source as the S3 bucket. Set the target endpoint as the AWS Glue workflow.

Discussion 0
Questions 51

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.  

Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

B.  

Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

C.  

Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

D.  

Write an AWS Glue Python shell job. Use pandas to transform the data.

Discussion 0
Questions 52

A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.

The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.  

Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.

B.  

Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.

C.  

Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.

D.  

Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.

Discussion 0
Questions 53

A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.

The data engineer must identify and remove duplicate information from the legacy application data.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop duplicatesf) function by importing the Pandas library to perform data deduplication.

B.  

Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.

C.  

Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

D.  

Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Discussion 0
Questions 54

A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.

The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.

Which combination of steps will meet these requirements with the LEAST development effort? (Select TWO.)

Options:

A.  

Use FluentBit to collect logs. Use OpenTelemetry to collect traces.

B.  

Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.

C.  

Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.

D.  

Use Amazon OpenSearch to correlate the logs and traces.

E.  

Use AWS Glue to correlate the logs and traces.

Discussion 0
Questions 55

A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.

Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.

Which solution will meet these requirements?

Options:

A.  

Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.

B.  

Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.

C.  

Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.

D.  

Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.

Discussion 0
Questions 56

A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.

Which AWS Glue feature should the data engineer use to meet this requirement?

Options:

A.  

Workflows

B.  

Triggers

C.  

Job bookmarks

D.  

Classifiers

Discussion 0
Questions 57

A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

Options:

A.  

Use AWS Glue DataBrew to read the files. Use the NEST TO ARRAY transformation to create the new column.

B.  

Use AWS Glue DataBrew to read the files. Use the NEST TO MAP transformation to create the new column.

C.  

Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.

D.  

Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.

Discussion 0
Questions 58

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

Options:

A.  

Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

B.  

Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

C.  

Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

D.  

Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Discussion 0
Questions 59

A company stores customer records in Amazon S3. The company must not delete or modify the customer record data for 7 years after each record is created. The root user also must not have the ability to delete or modify the data.

A data engineer wants to use S3 Object Lock to secure the data.

Which solution will meet these requirements?

Options:

A.  

Enable governance mode on the S3 bucket. Use a default retention period of 7 years.

B.  

Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.

C.  

Place a legal hold on individual objects in the S3 bucket. Set the retention period to 7 years.

D.  

Set the retention period for individual objects in the S3 bucket to 7 years.

Discussion 0
Questions 60

A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.

Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.

Which solution will meet these requirements in the MOST operationally efficient way?

Options:

A.  

Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.

B.  

Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3.

C.  

Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day Overwrite the previous day's full-load copy every day.

D.  

Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.

Discussion 0
Questions 61

A data engineer needs to create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.

Which query will meet this requirement?

Options:

A.  

CREATE TABLE new_table LIKE old_table;

B.  

CREATE TABLE new_table AS SELECT * FROM old_table WITH NO DATA;

C.  

CREATE TABLE new_table AS SELECT * FROM old_table;

D.  

CREATE TABLE new_table AS SELECT * FROM old_table WHERE 1=1;

Discussion 0
Questions 62

A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)

Options:

A.  

Select * from Employee where Region ID='North America';

B.  

Select * from Employee where Region ID='North America' and Department ID=20;

C.  

Select * from Employee where Department ID=20 and Region ID='North America';

D.  

Select " from Employee where Role ID=50;

E.  

Select * from Employee where Region ID='North America' and Role ID=50;

Discussion 0
Questions 63

A company wants to combine data from multiple software as a service (SaaS) applications for analysis.

A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.

Which solution will meet these requirements in the MOST operationally efficient way?

Options:

A.  

Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight

B.  

Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.

C.  

Use Amazon AppFlow to create a Row for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.

D.  

Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.

Discussion 0
Questions 64

A company generates reports from 30 tables in an Amazon Redshift data warehouse. The data source is an operational Amazon Aurora MySQL database that contains 100 tables. Currently, the company refreshes all data from Aurora to Redshift every hour, which causes delays in report generation.

Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

Options:

A.  

Use AWS Database Migration Service (AWS DMS) to create a replication task. Select only the required tables.

B.  

Create a database in Amazon Redshift that uses the integration.

C.  

Create a zero-ETL integration in Amazon Aurora. Select only the required tables.

D.  

Use query editor v2 in Amazon Redshift to access the data in Aurora.

E.  

Create an AWS Glue job to transfer each required table. Run an AWS Glue workflow to initiate the jobs every 5 minutes.

Discussion 0
Questions 65

A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.

A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day

B.  

Use the query result reuse feature of Amazon Athena for the SQL queries.

C.  

Add an Amazon ElastiCache cluster between the Bl application and Athena.

D.  

Change the format of the files that are in the dataset to Apache Parquet.

Discussion 0