QUESTION 21
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. Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.
B. Store the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.
C. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.
D. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.

Correct Answer: A

QUESTION 22
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes. Which solution on AWS will detect duplicates in the dataset with the LEAST operational overhead?

A. Use Amazon Mechanical Turk jobs to detect duplicates.
B. Use Amazon QuickSight ML Insights to build a custom deduplication model.
C. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
D. Use the AWS Glue FindMatches transform to detect duplicates.

Correct Answer: D

QUESTION 23
A company is using Amazon SageMaker AI 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 AI 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. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
B. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
D. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Correct Answer: C

QUESTION 24
An ML engineer needs to use Amazon SageMaker AI 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. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
B. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
C. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
D. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.

Correct Answer: D

QUESTION 25
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?

A. Redesign the ML services to be configured in Kubeflow. Deploy the new Kubeflow managed ML services to the EKS cluster.
B. Upload the Docker images to an Amazon Elastic Container Registry (Amazon ECR) repository. Configure a deployment pipeline to deploy the images to the EKS cluster.
C. Migrate the training data to an Amazon Redshift cluster. Retrain the models from the migrated training data by using Amazon Redshift ML. Deploy the retrained models to the EKS cluster.
D. Configure an Amazon SageMaker Al notebook. Retrain the models with the same code. Deploy the retrained models to the EKS cluster.

Correct Answer: B

QUESTION 26
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata. Which solution will meet these requirements with the LEAST manual effort?

A. Use AWS Glue to run the ETL job. Use the job to discover the schema and to store the associated metadata in the AWS Glue Data Catalog.
B. Create an Amazon SageMaker Data Wrangler flow to run the ETL job. Use the job to discover the schema and to store the associated metadata in an S3 bucket.
C. Create an ETL pipeline by using Amazon Athena integrated with AWS Step Functions. Use the pipeline to run the ETL job to discover the schema and to store the associated metadata in an S3 bucket.
D. Launch an Amazon EC2 instance that includes the scikit-learn library to run the ETL job. Use the job to discover the schema and to store the associated metadata in Amazon Redshift.

Correct Answer: A

QUESTION 27
A company has trained an ML model that is packaged in a container. The company will integrate the model with an existing Python web application. The company needs to host the model on AWS by using Kubernetes. The company does not want to manage the control plane and must provision the resources in a repeatable manner. The infrastructure must be provisioned by using Python. Which solution will meet these requirements?

A. Use AWS CloudFormation to provision Amazon EC2 instances in multiple Availability Zones. Set up a Kubernetes cluster. Host the model container on the Kubernetes cluster.
B. Use the AWS CLI to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.
C. Use the AWS Cloud Development Kit (AWS CDK) to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.
D. Use AWS CloudFormation to provision an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Store the image in an Amazon Elastic Container Registry (Amazon ECR) repository. Host the model container on the EKS cluster.

Correct Answer: C

QUESTION 28
An ML engineer is setting up an Amazon SageMaker Al pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected. How should the ML engineer set up the pipeline to meet this requirement?

A. Use an AWS Glue crawler and an AWS Glue extract, transform, and load (ETL) job to detect data drift. Use AWS Glue triggers to automate the re-training job.
B. Use Amazon Managed Service for Apache Flink to detect data drift. Use an AWS Lambda function to automate the re-training job.
C. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re-training job.
D. Use Amazon QuickSight anomaly detection to detect data drift. Use an AWS Step Functions workflow to automate the re-training job.

Correct Answer: C

QUESTION 29
An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset. The ML engineer wants the model to make continuous numeric predictions based on 10 years of data. Which metric should the ML engineer use to evaluate the model’s performance?

A. Accuracy
B. InferenceLatency
C. Area Under the ROC Curve (AUC)
D. Root mean square error (RMSE)

Correct Answer: D

QUESTION 30
An ML engineer has developed a custom scikit-learn script to transform tabular data to train a regression model. The ML engineer wants to use Amazon SageMaker AI to prepare data and to train and evaluate the model. Which solution will meet these requirements?

A. Create an AWS Lambda function to run the custom script. Load the processed data into SageMaker AI to train the model.
B. Use the SKLearnProcessor class within the SageMaker Python SDK to run the custom script.
C. Apply the HuggingFaceProcessor class within the SageMaker Python SDK to run the custom script.
D. Use AWS Glue ETL custom recipes to perform data transformations. Load the processed data into SageMaker AI to train the model.

Correct Answer: B

QUESTION 31
A company runs a neural network model and retrains the model when the performance degrades. The company uses a training job that uses Amazon SageMaker Al distributed data parallelism (DDP). The training job takes several hours to run. The company wants to decrease the required time for the training job. Which solution will meet this requirement?

A. Increase the number of epochs.
B. Increase the number of layers.
C. Increase the number of neurons in the hidden layers.
D. Increase the number of instances

Correct Answer: D

QUESTION 32
A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible on AWS. Which feature of SageMaker AI should the company use to meet these requirements?

A. SageMaker AI built-in algorithms
B. SageMaker Canvas
C. SageMaker JumpStart
D. SageMaker AI script mode

Correct Answer: D

QUESTION 33
A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames. The company needs to retrain the model with an existing training workflow in Amazon SageMaker Al. An ML engineer must implement a solution that decreases the labeling time. Which solution will meet these requirements?

A. Use SageMaker Ground Truth to annotate the video frames.
B. Use SageMaker JumpStart to use pre-trained computer vision models to develop a labeling model.
C. Use SageMaker Data Wrangler to create a data workflow. Use the workflow to optimize the labeling process.
D. Use the labeling interface of Amazon Augmented Al (Amazon A2l) with Amazon Rekognition to label the video frames.
Correct Answer: D

QUESTION 34
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support. Which modeling approach should the company use to meet this requirement?

A. Anomaly detection
B. Linear regression
C. Logistic regression
D. Semantic segmentation

Correct Answer: C

QUESTION 35
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon S3 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. Deploy the model on Amazon SageMaker AI. Create a set of AWS Lambda functions to identify and remove the sensitive data.
B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data
D. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

Correct Answer: C

QUESTION 36
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region. Which solution will meet this requirement with the LEAST development effort?

A. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B
B. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
C. Use AWS DataSync to replicate the model from Account A to Account B
D. Create an AWS Site-to-Site VPN connection between Account A and Account B to transfer the model.

Correct Answer: B

QUESTION 37
A hospital is using an ML model to validate x-ray results. The hospital runs a nightly batch inference job. The hospital needs to produce a daily report about model data quality and model performance. Which solution will meet these requirements?

A. Schedule a monitoring job in Amazon SageMaker Model Monitor. Generate the monitoring results for the model and data.
B. Create an Amazon CloudWatch dashboard that includes the metrics for processing steps in the nightly batch inference job. Compare the baseline resource metrics. Share the dashboard link.
C. Use AWS Glue DataBrew to create a custom recipe job that uses the Numerical Statistics data quality check for the model file. Generate the results.
D. Create a SageMaker Al pipeline that includes a QualityCheck step to run monitoring jobs. Generate the monitoring results for the model and the data.

Correct Answer: A

QUESTION 38
A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents. Which solution will meet this requirement with the LEAST operational effort?

A. Use Amazon Rekognition eye gaze direction detection to monitor driver behavior and identify distractions.
B. Use Amazon SageMaker Al to customize an Al model to monitor driver behavior and identify distractions.
C. Integrate a third-party driver monitoring system with Amazon Rekognition to monitor driver behavior and identify distractions.
D. Use Amazon Comprehend to analyze text-based driver feedback and identify distractions.

Correct Answer: A

QUESTION 39
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?

A. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
B. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.

Correct Answer: A

QUESTION 40
An ML engineer is collecting data to train a classification ML model by using Amazon SageMaker AI. The target column can have two possible values: Class A or Class B. The ML engineer wants to ensure that the number of samples for both Class A and Class B are balanced, without losing any existing training data. The ML engineer must test the balance of the training data. Which solution will meet this requirement?

A. Use SageMaker Clarify to check for class imbalance (CI). If the value is equal to 0, then use random undersampling in SageMaker Data Wrangler to balance the classes.
B. Use SageMaker Clarify to check for class imbalance (CI). If the value is greater than 0, then use synthetic minority oversampling technique (SMOTE) in SageMaker Data Wrangler to balance the classes.
C. Use SageMaker JumpStart to generate a class imbalance (CI) report. If the value is greater than 0, then use random undersampling in SageMaker Studio to balance the classes.
D. Use SageMaker JumpStart to generate a class imbalance (CI) report. If the value is equal to 0, then use synthetic minority oversampling technique (SMOTE) in SageMaker Studio to balance the classes.

Correct Answer: B