A company plans to use Amazon SageMaker AI to build image classification models. The company has 6 TB of training data stored on Amazon FSx for NetApp ONTAP. The file system is in the same VPC as SageMaker AI.

An ML engineer must make the training data accessible to SageMaker AI training jobs.

Which solution will meet these requirements?

A company has deployed a model to predict the churn rate for its games by using Amazon SageMaker Studio. After the model is deployed, the company must monitor the model performance for data drift and inspect the report. Select and order the correct steps from the following list to model monitor actions. Select each step one time. (Select and order THREE.) .

Check the analysis results on the SageMaker Studio console. .

Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.

Schedule an hourly model explainability monitor.

A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs from its own standalone Docker image.

The company needs to perform a lift and shift from the on-premises Kubernetes cluster to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

Which solution will meet this requirement with the LEAST operational overhead?

An ML engineer is using Amazon Quick Suite (previously known as Amazon QuickSight) anomaly detection to detect very high or very low machine operating temperatures compared to normal. The ML engineer sets the Severity parameter to Low and above. The ML engineer sets the Direction parameter to All.

What effect will the ML engineer observe in the anomaly detection results if the ML engineer changes the Direction parameter to Lower than expected?

A company regularly receives new training data from a vendor of an ML model. The vendor delivers cleaned and prepared data to the company’s Amazon S3 bucket every 3–4 days.

The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs to run the pipeline automatically when new data is uploaded to the S3 bucket.

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

A company uses an ML model to recommend videos to users. The model is deployed on Amazon SageMaker AI. The model performed well initially after deployment, but the model's performance has degraded over time.

Which solution can the company use to identify model drift in the future?

A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.

Which solution will meet these requirements in the LEAST amount of time?

A company uses a training job on Amazon SageMaker Al to train a neural network. The job first trains a model and then evaluates the model's performance ag

test dataset. The company uses the results from the evaluation phase to decide if the trained model will go to production.

The training phase takes too long. The company needs solutions that can shorten training time without decreasing the model's final performance.

Select the correct solutions from the following list to meet the requirements for each description. Select each solution one time or not at all. (Select THREE.)

. Change the epoch count.

. Choose an Amazon EC2 Spot Fleet.

· Change the batch size.

. Use early stopping on the training job.

· Use the SageMaker Al distributed data parallelism (SMDDP) library.

. Stop the training job.

A company must install a custom script on any newly created Amazon SageMaker AI notebook instances.

Which solution will meet this requirement with the LEAST operational overhead?

A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events.

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

A company needs to combine data from multiple sources. The company must use Amazon Redshift Serverless to query an AWS Glue Data Catalog database and underlying data that is stored in an Amazon S3 bucket.

Select and order the correct steps from the following list to meet these requirements. Select each step one time or not at all. (Select and order three.)

• Attach the IAM role to the Redshift cluster.

• Attach the IAM role to the Redshift namespace.

• Create an external database in Amazon Redshift to point to the Data Catalog schema.

• Create an external schema in Amazon Redshift to point to the Data Catalog database.

• Create an IAM role for Amazon Redshift to use to access only the S3 bucket that contains underlying data.

• Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.

A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends.

Which algorithm and hyperparameters should the company use to develop the model?

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.

Which solution will meet this requirement?

A company uses AWS CodePipeline to orchestrate a continuous integration and continuous delivery (CI/CD) pipeline for ML models and applications.

Select and order the steps from the following list to describe a CI/CD process for a successful deployment. Select each step one time. (Select and order FIVE.)

. CodePipeline deploys ML models and applications to production.

· CodePipeline detects code changes and starts to build automatically.

. Human approval is provided after testing is successful.

. The company builds and deploys ML models and applications to staging servers for testing.

. The company commits code changes or new training datasets to a Git repository.

An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of data quality for the models and must receive alerts when changes in data quality occur.

Which solution will meet these requirements?

An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The model must make continuous numeric predictions based on 10 years of data.

Which metric should the ML engineer use to evaluate the model’s performance?

A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and display the results on a dashboard. An ML engineer creates a bias monitoring job.

How should the ML engineer capture bias metrics to display on the dashboard?

A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.

Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

An ML engineer needs to use an ML model to predict the price of apartments in a specific location.

Which metric should the ML engineer use to evaluate the model’s performance?

A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.

Which solution will meet this requirement?

A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.

Which solution will meet these requirements?

An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.

Which file format will meet these requirements?

A company is building a conversational AI assistant on Amazon Bedrock. The company is using Retrieval Augmented Generation (RAG) to reference the company's internal knowledge base. The AI assistant uses the Anthropic Claude 4 foundation model (FM).

The company needs a solution that uses a vector embedding model, a vector store, and a vector search algorithm.

Which solution will develop the AI assistant with the LEAST development effort?

An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.

Which instance purchasing option will meet these requirements MOST cost-effectively?

A company is developing an application that reads animal descriptions from user prompts and generates images based on the information in the prompts. The application reads a message from an Amazon Simple Queue Service (Amazon SQS) queue. Then the application uses Amazon Titan Image Generator on Amazon Bedrock to generate an image based on the information in the message. Finally, the application removes the message from SQS queue.

Which IAM permissions should the company assign to the application's IAM role? (Select TWO.)

An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.

Which solution will meet these requirements?

A company is developing a generative AI conversational interface to assist customers with payments. The company wants to use an ML solution to detect customer intent. The company does not have training data to train a model.

Which solution will meet these requirements?

An ML engineer needs to organize a large set of text documents into topics. The ML engineer will not know what the topics are in advance. The ML engineer wants to use built-in algorithms or pre-trained models available through Amazon SageMaker AI to process the documents.

Which solution will meet these requirements?

An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a retraining job if any data drift is detected.

How should the ML engineer set up the pipeline to meet this requirement?

A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.

Which solution will meet these requirements in the LEAST amount of time?

A company needs to ingest data from data sources into Amazon SageMaker Data Wrangler. The data sources are Amazon S3, Amazon Redshift, and Snowflake. The ingested data must always be up to date with the latest changes in the source systems.

Which solution will meet these requirements?

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

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

An ML engineer decides to use Amazon SageMaker AI automated model tuning (AMT) for hyperparameter optimization (HPO). The ML engineer requires a tuning strategy that uses regression to slowly and sequentially select the next set of hyperparameters based on previous runs. The strategy must work across small hyperparameter ranges.

Which solution will meet these requirements?

An ML engineer is analyzing a classification dataset before training a model in Amazon SageMaker AI. The ML engineer suspects that the dataset has a significant imbalance between class labels that could lead to biased model predictions. To confirm class imbalance, the ML engineer needs to select an appropriate pre-training bias metric.

Which metric will meet this requirement?

An ML engineer is training an ML model to identify medical patients for disease screening. The tabular dataset for training contains 50,000 patient records: 1,000 with the disease and 49,000 without the disease.

The ML engineer splits the dataset into a training dataset, a validation dataset, and a test dataset.

What should the ML engineer do to transform the data and make the data suitable for training?

An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.

The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.

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

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.

Which algorithm should the ML engineer use to meet this requirement?

A company has a custom extract, transform, and load (ETL) process that runs on premises. The ETL process is written in the R language and runs for an average of 6 hours. The company wants to migrate the process to run on AWS.

Which solution will meet these requirements?

A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.

The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.

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

A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.

An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.

Which solution will meet these requirements?

A company wants to build an anomaly detection ML model. The model will use large-scale tabular data that is stored in an Amazon S3 bucket. The company does not have expertise in Python, Spark, or other languages for ML.

An ML engineer needs to transform and prepare the data for ML model training.

Which solution will meet these requirements?

A company wants to improve its customer retention ML model. The current model has 85% accuracy and a new model shows 87% accuracy in testing. The company wants to validate the new model’s performance in production.

Which solution will meet these requirements?

An ML engineer is using an Amazon SageMaker AI shadow test to evaluate a new model that is hosted on a SageMaker AI endpoint. The shadow test requires significant GPU resources for high performance. The production variant currently runs on a less powerful instance type.

The ML engineer needs to configure the shadow test to use a higher performance instance type for a shadow variant. The solution must not affect the instance type of the production variant.

Which solution will meet these requirements?

A company has multiple models that are hosted on Amazon SageMaker Al. The models need to be re-trained. The requirements for each model are different, so the company needs to choose different deployment strategies to transfer all requests to a new model.

Select the correct strategy from the following list for each requirement. Select each strategy one time. (Select THREE.)

. Canary traffic shifting

. Linear traffic shifting guardrail

. All at once traffic shifting

A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.

Which solution will meet these requirements?

A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.

A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.

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

A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.

Which solution will meet these requirements?

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.

During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.

What could be the reason for the reduced F1 score?

A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.

What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.

The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.

Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.

Which solution will meet these requirements?

A company ingests sales transaction data using Amazon Data Firehose into Amazon OpenSearch Service. The Firehose buffer interval is set to 60 seconds.

The company needs sub-second latency for a real-time OpenSearch dashboard.

Which architectural change will meet this requirement?

A company uses an Amazon SageMaker AI model for real-time inference with auto scaling enabled. During peak usage, new instances launch before existing instances are fully ready, causing inefficiencies and delays.

Which solution will optimize the scaling process without affecting response times?

A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.

The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.

Which metric should the ML engineer use for the model recalibration?

An ML engineer is preparing a dataset that contains medical records to train an ML model to predict the likelihood of patients developing diseases.

The dataset contains columns for patient ID, age, medical conditions, test results, and a "Disease" target column.

How should the ML engineer configure the data to train the model?

An ML engineer has a custom container that performs k-fold cross-validation and logs an average F1 score during training. The ML engineer wants Amazon SageMaker AI Automatic Model Tuning (AMT) to select hyperparameters that maximize the average F1 score.

How should the ML engineer integrate the custom metric into SageMaker AI AMT?

A company wants to improve the sustainability of its ML operations.

Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.

Which solution will meet these requirements?

A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.

An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.

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

A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.

The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.

Which change to the architecture will meet these requirements?

An ML engineer is using Amazon SageMaker AI to train an ML model. The ML engineer needs to use SageMaker AI automatic model tuning (AMT) features to tune the model hyperparameters over a large parameter space.

The model has 20 categorical hyperparameters and 7 continuous hyperparameters that can be tuned. The ML engineer needs to run the tuning job a maximum of 1,000 times. The ML engineer must ensure that each parameter trial is built based on the performance of the previous trial.

Which solution will meet these requirements?

An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.

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