A.  

They can set up separate expectations for each table when developing their DLT pipeline.

B.  

They cannot determine which table is dropping the records.

C.  

They can set up DLT to notify them via email when records are dropped.

D.  

They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

E.  

They can navigate to the DLT pipeline page, click on the “Error” button, and review the present errors.

A.  

Cloud-specific integrations

B.  

Simplified governance

C.  

Ability to scale storage

D.  

Ability to scale workloads

E.  

Avoiding vendor lock-in

A.  

CREATE FUNCTION main.secure.region_filter(region STRING)

RETURN IF(is_account_group_member( ' all_regions ' ), true, region = ' EU ' );

ALTER TABLE main.secure.events

SET ROW FILTER main.secure.region_filter ON (region);

B.  

CREATE FUNCTION main.secure.region_filter(region STRING)

RETURN IF(is_account_group_member( ' all_regions ' ), true, region = ' EU ' );

ALTER TABLE main.secure.events

ALTER COLUMN region

SET MASK main.secure.region_filter;

C.  

CREATE FUNCTION main.secure.region_filter(region STRING)

RETURN IF(is_account_group_member( ' all_regions ' ), true, region = ' EU ' );

ALTER VIEW main.secure.events

SET ROW FILTER main.secure.region_filter ON (region);

D.  

CREATE FUNCTION main.secure.region_filter(region STRING)

RETURN IF(is_account_group_member( ' all_regions ' ), true, region = ' EU ' );

ALTER TABLE main.secure.events

SET ROW FILTER main.secure.region_filter USING COLUMNS (region);

A.  

resources:

jobs:

hello-job:

name: hello-job

tasks:

- task_key: hello-task

existing_cluster_id: 1234-567890-abcde123

notebook_task:

notebook_path: ./hello.py

B.  

{

" resources " : {

" jobs " : {

" name " : " hello-job " ,

" tasks " : {

" task_key " : " hello-task " ,

" existing_cluster_id " : " 1234-567890-abcde123 " ,

" notebook_task " : {

" notebook_path " : " ./hello.py "

}

}

}

}

}

C.  

configuration = {

" resources " : {

" jobs " : {

" name " : " hello-job " ,

" tasks " : {

" task_key " : " hello-task " ,

" existing_cluster_id " : " 1234-567890-abcde123 " ,

" notebook_task " : {

" notebook_path " : " ./hello.py "

}

}

}

}

}

D.  

resources {

jobs {

name = " hello-job "

tasks {

task_key = " hello-task "

existing_cluster_id = " 1234-567890-abcde123 "

notebook_task {

notebook_path = " ./hello.py "

}

}

}

}

A.  

Repair the task

B.  

Rerun the pipeline

C.  

Restart the Cluster

D.  

Switch the cluster

A.  

Checkpointing and Write-ahead Logs

B.  

Structured Streaming cannot record the offset range of the data being processed in each trigger.

C.  

Replayable Sources and Idempotent Sinks

D.  

Write-ahead Logs and Idempotent Sinks

E.  

Checkpointing and Idempotent Sinks

A.  

Configure the parallel tasks with sequential dependencies so that each task waits for the previous task to finish.

B.  

Add the dashboard task as dependent on all three parallel tasks using fan-in control flow.

C.  

Set the dashboard task to depend only on the SQL query task; the other tasks run independently.

D.  

Create separate Lakeflow Jobs for each parallel task and trigger them sequentially using external orchestration.

A.  

Implement a hybrid approach with scheduled batch jobs on custom cloud VMs.

B.  

Use an auto-scaling cluster configured and monitored by the user.

C.  

Utilize Databricks serverless compute that automatically optimizes resources and abstracts cluster management.

D.  

Deploy a dedicated, manually managed cluster optimized by in-house IT staff.

A.  

Switch to another cluster

B.  

Repair run

C.  

Re-run the entire workflow

D.  

Restart the cluster

A.  

Add an If/else condition task that reads the record_count task value and routes execution to different downstream tasks.

B.  

Configure additional retries for check_volume until the count exceeds the threshold.

C.  

Use a For each task over record_count and stop when the value is below the threshold.

D.  

Configure Run if conditions based purely on task status.

A.  

A key-value pair configuration

B.  

The preferred DBU/hour cost

C.  

A path to cloud storage location for the written data

D.  

A location of a target database for the written data

E.  

At least one notebook library to be executed

A.  

It further reduces file sizes by re-clustering the data

B.  

Triggers a full liquid clustering process

C.  

Changes the number of tuples per file significantly

D.  

It has no effect because it is idempotent.

A.  

Ingestion fails with a schema-mismatch error until the table is manually altered.

B.  

All existing data is backfilled with a default value for userAgent, and the column is made non-nullable.

C.  

The userAgent field is added as a new nullable column in the Delta table and populated for new data.

D.  

The userAgent field is silently ignored, and its data is dropped from all new files.

A.  

They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.

B.  

They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.

C.  

They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.

D.  

There is no way to determine why a Job task is running slowly.

E.  

They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.

A.  

Silver tables contain a less refined, less clean view of data than Bronze data.

B.  

Silver tables contain aggregates while Bronze data is unaggregated.

C.  

Silver tables contain more data than Bronze tables.

D.  

Silver tables contain a more refined and cleaner view of data than Bronze tables.

E.  

Silver tables contain less data than Bronze tables.

A.  

df.groupBy( " customer_id " , " email " ).agg(max( " ingestion_timestamp " ).alias( " latest_ts " ))

B.  

df.distinct()

C.  

df.select( " customer_id " , " email " ).distinct()

D.  

df.dropDuplicates([ " customer_id " , " email " ])

A.  

Use a single connector with batch scheduling, a shared Unity Catalog schema, and incremental snapshots stored externally.

B.  

Use a single Lakeflow connector for all three sources, write directly to Delta tables, and disable table versioning to reduce storage costs.

C.  

Use separate connectors with full-load synchronization only, archive historical data separately, and create external tables for audit queries.

D.  

Use separate connectors for each source, source-appropriate incremental processing, individual Unity Catalog schemas, and retained Delta table versions for audit compliance.

A.  

They can clone the existing task in the existing Job and update it to run the new notebook.

B.  

They can create a new task in the existing Job and then add it as a dependency of the original task.

C.  

They can create a new task in the existing Job and then add the original task as a dependency of the new task.

D.  

They can create a new job from scratch and add both tasks to run concurrently.

E.  

They can clone the existing task to a new Job and then edit it to run the new notebook.

A.  

Parquet files can be partitioned

B.  

CREATE TABLE AS SELECT statements cannot be used on files

C.  

Parquet files have a well-defined schema

D.  

Parquet files have the ability to be optimized

E.  

Parquet files will become Delta tables

A.  

Job cluster with Photon enabled

B.  

Interactive cluster

C.  

Job cluster with spot instances disabled

D.  

Job cluster with spot instances enabled

A.  

Upsize the worker nodes and activate autoshuffle partitions

B.  

Upsize the driver node and deactivate autoshuffle partitions

C.  

Cache the dataset in order to boost the query performance

D.  

Fix the shuffle partitions to 50 to ensure the allocation

A.  

Use ODBC pooling to extract all records in one batch and write them to /dbfs/

B.  

Use spark.read.format( " jdbc " ) and write the resulting DataFrame with saveAsTable( " catalog.schema.table " )

C.  

Stage the data in cloud storage through a REST API and load it with a separate job

D.  

Extract the data to a Pandas DataFrame, convert it to Spark, and use save() to write it to an external path

A.  

The cluster spot-instance availability in the cloud provider

B.  

The SQL warehouse permissions for the task

C.  

The job-level max_concurrent_runs setting and whether queueing is enabled

D.  

The task-level timeout_seconds configuration

A.  

Category_sales = sales df.groupBy( " category " ).agg(sum( " sales amount " ) .alias ( " total sales amount " ))

B.  

Category_sales = sales_df.sum( " 3ales_amount " ). g-1- upBy( " categcryn).alias( " toLal_sales_amount))

C.  

Category_sale: .es df -agg (sum ( " sales amount " ) .-;r*i:rRy ( " category " ) .alias ( " total sa.en amount " ))

D.  

Category_sales = sales_df.groupBy( " reqion " ). agq(sum( " sales_amountn).alias(ntotal_sales_amount ' ' ))

A.  

When another task needs to be replaced by the new task

B.  

When another task needs to fail before the new task begins

C.  

When another task has the same dependency libraries as the new task

D.  

When another task needs to use as little compute resources as possible

E.  

When another task needs to successfully complete before the new task begins

A.  

Replace df.dropDuplicates() with df.groupBy( " user_id " , " event_type " ).count().

B.  

Replace df.dropDuplicates() with df.dropDuplicates([ " user_id " , " event_type " ]).

C.  

Replace df.dropDuplicates() with df.drop( " event_timestamp " ).dropDuplicates().

D.  

Replace df.dropDuplicates() with df.distinct().select( " user_id " , " event_type " ).

A.  

COMMENT " Contains PIT

B.  

511

C.  

" COMMENT PII "

D.  

TBLPROPERTIES PII

A.  

Automatically increase the cluster size whenever execution time exceeds the previous run’s duration.

B.  

Compare cluster metrics in the Lakeflow Jobs run history to identify resource spikes that are causing delays.

C.  

Archive old job runs and reset the baseline counter to ensure an accurate comparison of current performance.

D.  

Rerun the job immediately to establish a new baseline because historical data becomes irrelevant after seven days.

A.  

Read-optimized

B.  

Normalised

C.  

Raw Data

D.  

Historical lineage

E.  

De-normalised

A.  

DROP TABLE employees;

B.  

INSERT INTO employees (id, name) VALUES (1, ' Alice ' );

C.  

CRFATF tabif employees ( id INT, name suing

D.  

ALTFR TABIF employees add column salary DECTMA(10,2);

A.  

CREATE TABLE all_transactions ASSELECT * FROM march_transactionsINNER JOIN SELECT * FROM april_transactions;

B.  

CREATE TABLE all_transactions ASSELECT * FROM march_transactionsUNION SELECT * FROM april_transactions;

C.  

CREATE TABLE all_transactions ASSELECT * FROM march_transactionsOUTER JOIN SELECT * FROM april_transactions;

D.  

CREATE TABLE all_transactions ASSELECT * FROM march_transactionsINTERSECT SELECT * from april_transactions;

E.  

CREATE TABLE all_transactions ASSELECT * FROM march_transactionsMERGE SELECT * FROM april_transactions;

A.  

Databricks Command Line Interface

B.  

Databricks Asset Bundles

C.  

Databricks Connect

D.  

Databricks Software Development Kit

A.  

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

B.  

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

C.  

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

D.  

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

A.  

Delta tables are stored in a single file that contains data, history, metadata, and other attributes.

B.  

Delta tables store their data in a single file and all metadata in a collection of files in a separate location.

C.  

Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.

D.  

Delta tables are stored in a collection of files that contain only the data stored within the table.

E.  

Delta tables are stored in a single file that contains only the data stored within the table.

A.  

They can increase the cluster size of the SQL endpoint.

B.  

They can increase the maximum bound of the SQL endpoint’s scaling range.

C.  

They can turn on the Auto Stop feature for the SQL endpoint.

D.  

They can turn on the Serverless feature for the SQL endpoint.

E.  

They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.”

A.  

A data lakehouse provides storage solutions for structured and unstructured data.

B.  

A data lakehouse supports ACID-compliant transactions.

C.  

A data lakehouse allows the use of SQL queries to examine data.

D.  

A data lakehouse stores data in open formats.

E.  

A data lakehouse enables machine learning and artificial Intelligence workloads.

A.  

Share the dataset using Delta Sharing, which allows your partner to access the data using a secure, read-only URL without requiring a Databricks account, ensuring that they cannot modify the data.

B.  

Share the dataset using Unity Catalog, ensuring that both teams have full write access to the data within the same organization.

C.  

Share the dataset by exporting it to a CSV file and manually transferring the file to the partner ' s system.

D.  

Grant your partner access to your Databricks workspace and assign them full write permissions to the Delta table, enabling them to modify the dataset.

A.  

There was a type mismatch between the specific schema and the inferred schema

B.  

JSON data is a text-based format

C.  

Auto Loader only works with string data

D.  

All of the fields had at least one null value

E.  

Auto Loader cannot infer the schema of ingested data

A.  

Data

B.  

Cluster management metadata

C.  

Databricks web application

D.  

Notebooks

A.  

databricks bundle deploy run

B.  

databricks jobs submit --json < config >

C.  

databricks bundle execute etl_job

D.  

databricks bundle run etl_job

A.  

DESCRIBE LOCATION customer360;

B.  

DROP DATABASE customer360;

C.  

DESCRIBE DATABASE customer360;

D.  

ALTER DATABASE customer360 SET DBPROPERTIES ( ' location ' = ' /user ' };

E.  

USE DATABASE customer360;

A.  

O They can reduce the cluster size of the SQL endpoint.

B.  

Q They can turn on the Auto Stop feature for the SQL endpoint.

C.  

O They can set up the dashboard ' s SQL endpoint to be serverless.

D.  

0 They can ensure the dashboard ' s SQL endpoint matches each of the queries ' SQL endpoints.

A.  

The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

B.  

The names of the files to be copied were not included with the FILES keyword.

C.  

The previous day’s file has already been copied into the table.

D.  

The PARQUET file format does not support COPY INTO.

E.  

The COPY INTO statement requires the table to be refreshed to view the copied rows.

A.  

There is no way to indicate whether a table contains PII.

B.  

" COMMENT PII "

C.  

TBLPROPERTIES PII

D.  

COMMENT " Contains PII "

E.  

PII

A.  

On schema campaigns, grant USE SCHEMA and SELECT; then grant SELECT on table email_stats.

B.  

On catalog corp_marketing, grant USE CATALOG and CREATE SCHEMA; on schema campaigns, grant USE SCHEMA; then grant SELECT on email_stats.

C.  

On catalog corp_marketing, grant USE CATALOG; on schema campaigns, grant USE SCHEMA; then grant SELECT on email_stats.

D.  

On schema campaigns, grant USE SCHEMA; then grant SELECT and MODIFY on email_stats.

A.  

Use partner connectors such as Fivetran for all three sources through Partner Connect

B.  

Use Auto Loader for all three sources, with custom Python code for Salesforce API calls and SQL Server CDC extraction

C.  

Use Lakeflow Connect managed connectors for Salesforce and SQL Server, and Auto Loader for the ADLS files, within Lakeflow Spark Declarative Pipelines

D.  

Use Lakeflow Connect managed connectors for all three sources

A.  

By allowing direct execution of Spark jobs from the local machine without needing a network connection

B.  

By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using a specific IDE that is required by Databricks

C.  

By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using their preferred ide

D.  

By providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code only through Databricks ' own web interface

A.  

Parquet

B.  

Delta

C.  

SV

D.  

JSON

A.  

They can schedule the query to refresh every 1 day from the SQL endpoint ' s page in Databricks SQL.

B.  

They can schedule the query to refresh every 12 hours from the SQL endpoint ' s page in Databricks SQL.

C.  

They can schedule the query to refresh every 1 day from the query ' s page in Databricks SQL.

D.  

They can schedule the query to run every 1 day from the Jobs UI.

E.  

They can schedule the query to run every 12 hours from the Jobs UI.

A.  

When the location of the data needs to be changed

B.  

When the target table is an external table

C.  

When the source table can be deleted

D.  

When the target table cannot contain duplicate records

E.  

When the source is not a Delta table

A.  

Option A

B.  

Option B

C.  

Option C

D.  

Option D

A.  

Inside the README.md file in the bundle’s root directory

B.  

Within the Python source code of the Lakeflow pipeline

C.  

In the targets section of the databricks.yml file

D.  

In a global environment variable on the local development machine

A.  

Use a Lakeflow Connect standard connector, implement MERGE-based CDC, schema evolution, and retries in workflows, and create a dedicated monitoring dashboard.

B.  

Use Auto Loader on exported SaaS files and a Lakeflow Spark Declarative Pipeline that calls the SaaS APIs, handling CDC, schema changes, OAuth, and retries in custom code.

C.  

Schedule regional batch jobs that call SaaS REST APIs and write directly to Silver tables using custom CDC and schema-evolution logic.

D.  

Use a Lakeflow Connect managed connector for the SaaS application with built-in CDC, schema evolution, OAuth, and serverless compute with retries, and process the data downstream using Lakeflow Spark Declarative Pipelines.

A.  

None of these lines of code are needed to successfully complete the task

B.  

USING CSV

C.  

FROM CSV

D.  

USING DELTA

E.  

FROM " path/to/csv "

A.  

PIVOT

B.  

CONVERT

C.  

WHERE

D.  

TRANSFORM

E.  

SUM

A.  

Use Serverless compute in Databricks to automatically scale and provision resources with minimal manual intervention

B.  

Deploy job clusters with fixed configurations, dedicated to specific tasks, without automatic scaling

C.  

Use spot instances to allocate resources dynamically while minimizing costs, with potential interruptions

D.  

Use interactive clusters in Databricks, adjusting cluster sizes manually based on workload demands

A.  

None of these changes will need to be made

B.  

The pipeline will need to stop using the medallion-based multi-hop architecture

C.  

The pipeline will need to be written entirely in SQL

D.  

The pipeline will need to use a batch source in place of a streaming source

E.  

The pipeline will need to be written entirely in Python

A.  

Perform extensive data cleansing

B.  

Aggregate data from multiple sources

C.  

Business rules and transformations

D.  

Include columns Load date/time, process ID

A.  

An ability to work with data in a variety of types at once

B.  

An ability to work with data within certain partitions and windows

C.  

An ability to work with time-related data in specified intervals

D.  

An ability to work with complex, nested data ingested from JSON files

E.  

An ability to work with an array of tables for procedural automation

A.  

spark.sql.shuffle.partitions is too high.

B.  

spark.memory.fraction is too low.

C.  

spark.executor.memory is too low.

D.  

spark.driver.memory is too low to hold the collected result.

A.  

Cloud File Storage

B.  

Unity Catalog

C.  

Data lake

D.  

Delta Lake

A.  

All-purpose cluster

B.  

Job cluster

C.  

Serverless compute

D.  

SQL Warehouse

A.  

Unity Catalog lineage visualizes dependencies between Delta tables, notebooks, and jobs, but does not provide column-level tracing or relationships with dashboards.

B.  

Unity Catalog lineage only supports visualizing relationships at the table level and does not extend to notebooks, jobs, or dashboards.

C.  

Unity Catalog lineage provides an interactive graph that tracks dependencies between tables and notebooks but excludes any job-related dependencies or dashboard visualizations.

D.  

Unity Catalog provides an interactive graph that visualizes the dependencies between Delta tables, notebooks, jobs, and dashboards, while also supporting column-level tracking of data transformations.

A.  

Databricks SQL Analytics

B.  

Databricks Jobs

C.  

Databricks Runtime for ML

D.  

Serverless SQL Warehouse

A.  

TRANSFORM

B.  

PIVOT

C.  

SUM

D.  

CONVERT

A.  

Their Databricks account password

B.  

The name of their Databricks cluster

C.  

The IP address of their Databricks workspace

D.  

The sharing identifier of their Unity Catalog metastore

A.  

Drop all tables; Databricks will only remove metadata for both managed and external tables

B.  

Drop managed tables that are ephemeral and drop external tables; files for both remain for 7 days

C.  

Drop managed staging tables to remove data and metadata, and drop external tables to remove only metadata

D.  

Drop external tables first to delete their files, then drop managed tables to keep their files for recovery

A.  

Databricks supports multiple languages but only one per notebook.

B.  

Databricks supports language interoperability in the same cell but only between Scala and SQL

C.  

Databricks supports language interoperability but only if a special character is used.

D.  

Databricks supports one language per cell.

A.  

Managed tables so that Unity Catalog manages both metadata and underlying data lifecycle

B.  

External tables with a LOCATION pointing to an external volume for full control of file layout

C.  

Foreign tables federated from an external catalog to delegate optimization to the source system

D.  

Temporary views over files to avoid table-level governance and lifecycle coupling