MLA-C01 practice questions 51–60

1. Question 51 · 226 in bank

   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.
   • DAdd the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.

   Show answer

   Answer: C

   Explanation

   I pick C. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. Its needs to RECORD all events.

   Source: Adapted from community discussion — a4002bd

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2. Question 52 · 226 in bank

   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.
   • DUse Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.

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   Answer: C

   References

   • AWS documentation

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3. Question 53 · 226 in bank

   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.
   • 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.

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   Answer: A

   References

   • AWS documentation

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4. Question 54 · 226 in bank

   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.

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   Answer: D

   References

   • AWS documentation

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5. Question 55 · 226 in bank

   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.

   Show answer

   Answer: D

   Explanation

   Shadow testing is a technique used to evaluate a new model's performance by running it alongside the current production model, processing the same live data but without affecting production outcomes.

   Source: Adapted from community discussion — Saransundar

   References

   • AWS documentation

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6. Question 56 · 226 in bank

   HOTSPOT - A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models. Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.) • Create an Amazon SageMaker batch transform job for data cleaning and feature engineering. • Store the resulting data back in Amazon S3. • Use Amazon Athena to infer the schemas and available columns. • Use AWS Glue crawlers to infer the schemas and available columns. • Use AWS Glue DataBrew for data cleaning and feature engineering.

   

   This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.

   Show the answer figure

   Answer: See figure below

   Explanation

   Order of steps: Use AWS Glue crawlers to infer schemas and available columns. Use AWS Glue DataBrew for data cleaning and feature engineering. Store the resulting data back in Amazon S3.

   Source: Adapted from community discussion — AzureDP900

   

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7. Question 57 · 226 in bank

   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.
   • 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.

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   Answer: C

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8. Question 58 · 226 in bank

   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.
   • 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.

   Show answer

   Answer: A

   Explanation

   Sagemaker Real-Time Inference - Faster predictions to solve delay issues; Model Monitor to tracks model quality and sends alerts for deviations

   Source: Adapted from community discussion — Saransundar

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9. Question 59 · 226 in bank

   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.

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   Answer: D

   References

   • AWS documentation

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10. Question 60 · 226 in bank

    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.
    • 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.

    Show answer

    Answer: B

    References

    • AWS documentation

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MLA-C01 practice, question 51. 226 questions available.