🔍

MLA-C01 — questions

Page 3 of 12 · 226 total questions.

MLA-C01 Question 41

Single page →
Topic 1 · Question 41

An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host. Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

  • AAWS::SageMaker::Model (correct answer)
  • BAWS::SageMaker::Endpoint
  • CAWS::SageMaker::NotebookInstance
  • DAWS::SageMaker::Pipeline
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: AWS::SageMaker::Model

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 42

Single page →
Topic 1 · Question 42

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?

  • AConfigure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
  • BStore users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
  • CUse Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns. (correct answer)
  • DConfigure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. AWS Glue is a serverless ETL and data-catalog service that prepares and integrates data. AWS Lake Formation builds and secures data lakes quickly.

MLA-C01 Question 43

Single page →
Topic 1 · Question 43

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?

  • ACSV files compressed with Snappy
  • BJSON objects in JSONL format
  • CJSON files compressed with gzip
  • DApache Parquet files (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Apache Parquet files.

MLA-C01 Question 44

Single page →
Topic 1 · Question 44

An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions. Which metric finding should the ML engineer prioritize the MOST when choosing the model?

  • ALow precision
  • BHigh precision
  • CLow recall
  • DHigh recall (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: High recall.

MLA-C01 Question 45

Single page →
Topic 1 · Question 45

A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold. Which solution will meet these requirements?

  • AUse SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
  • BUse SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.
  • CLog all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached. (correct answer)
  • DAdd the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.

Explanation

Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health. AWS CloudTrail records API activity for auditing and governance.

MLA-C01 Question 46

Single page →
Topic 1 · Question 46

A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually. The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines. Which solution will meet these requirements with the LEAST operational overhead?

  • AUse AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
  • BUse processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
  • CUse Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running. (correct answer)
  • DUse Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.

Explanation

AWS Glue is a serverless ETL and data-catalog service that prepares and integrates data. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 47

Single page →
Topic 1 · Question 47

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?

  • AConfigure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time. (correct answer)
  • BCreate a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
  • CUnload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.
  • DUnload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.

Explanation

AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 48

Single page →
Topic 1 · Question 48

An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems. The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training. Which solution will meet these requirements with the LEAST operational overhead?

  • AUse TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
  • BUse Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  • CExpand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  • DUse SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 49

Single page →
Topic 1 · Question 49

A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users. Which solution will meet these requirements?

  • ASet up SageMaker Debugger and create a custom rule.
  • BSet up blue/green deployments with all-at-once traffic shifting.
  • CSet up blue/green deployments with canary traffic shifting.
  • DSet up shadow testing with a shadow variant of the new model. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Set up shadow testing with a shadow variant of the new model.

MLA-C01 Question 50

Single page →
Topic 1 · Question 50

A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models. The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data. Which solution will provide the HIGHEST performance for data retrieval?

  • AKeep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.
  • BCreate AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.
  • COrganize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval. (correct answer)
  • DPut each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.

Explanation

Amazon S3 Glacier offers low-cost archival storage for infrequently accessed data. S3 Lifecycle policies automatically transition or expire objects to control storage cost. Amazon S3 provides durable, scalable object storage that is fully managed.

MLA-C01 Question 51

Single page →
Topic 1 · Question 51

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?

  • AUse SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality. (correct answer)
  • BUse SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.
  • CUse SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
  • DKeep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 52

Single page →
Topic 1 · Question 52

An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day. The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage. Which solution will meet these requirements?

  • ACreate AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
  • BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.
  • CDeploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
  • DDeploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.

Explanation

Auto Scaling adjusts capacity automatically to match demand, improving availability and cost efficiency. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health.

MLA-C01 Question 53

Single page →
Topic 1 · Question 53

A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold. Which solution will meet these requirements?

  • AAdd resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
  • BAdd resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached. (correct answer)
  • CAdd resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
  • DAdd resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance. AWS Budgets alerts on cost and usage thresholds.

MLA-C01 Question 54

Single page →
Topic 1 · Question 54

A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required. What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

  • ADownload the file to a local workstation. Perform one-hot encoding by using a custom Python script.
  • BCreate an Apache Spark job that uses a custom processing script on Amazon EMR.
  • CCreate a SageMaker processing job by calling the SageMaker Python SDK.
  • DCreate a data flow in SageMaker Data Wrangler. Configure a transform step. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Create a data flow in SageMaker Data Wrangler. Configure a transform step.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 55

Single page →
Topic 1 · Question 55

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?

  • AConfigure the competitor's name as a blocked phrase in Amazon Q Business. (correct answer)
  • BConfigure an Amazon Q Business retriever to exclude the competitor’s name.
  • CConfigure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
  • DConfigure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Configure the competitor's name as a blocked phrase in Amazon Q Business.

Explanation

AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 56

Single page →
Topic 1 · Question 56

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?

  • AUse SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
  • BUse SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
  • CUse SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
  • DUse SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 57

Single page →
Topic 1 · Question 57

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running. How should the company deploy the model on Amazon SageMaker to meet these requirements?

  • AUse a multi-model serverless endpoint. Enable caching.
  • BUse an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
  • CUse a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
  • DUse a serverless inference endpoint. Set the MaxConcurrency parameter to 1. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.

MLA-C01 Question 58

Single page →
Topic 1 · Question 58

An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents. The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras. Which solution will improve the model's accuracy in the LEAST amount of time?

  • ACollect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
  • BRecreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option. (correct answer)
  • CRecreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
  • DRecreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.

MLA-C01 Question 59

Single page →
Topic 1 · Question 59

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?

  • AStore the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation. (correct answer)
  • BStore the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.
  • CStore the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.
  • DStore the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.

Explanation

AWS Lambda runs code without provisioning servers and scales automatically, which minimizes operational overhead. AWS Secrets Manager stores and automatically rotates secrets such as database credentials.

MLA-C01 Question 60

Single page →
Topic 1 · Question 60

An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML. Which solution will meet these requirements?

  • AUse Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data. (correct answer)
  • BUse Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
  • CManually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
  • DManually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

Showing questions 41–60 of 226 · Page 3 of 12