MLA-C01 practice questions 41–50

1. Question 41 · 226 in bank

   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?

   • AUse zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
   • BReplace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
   • CIncrease the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
   • DReplace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.

   Show answer

   Answer: A

   Explanation

   A. Use zero buffering to minimize latency by delivering data immediate. Tune batch size to optimize throughput & ensures sub-second delivery for real-time dashboards.

   Source: Adapted from community discussion — Saransundar

   References

   • AWS documentation

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2. Question 42 · 226 in bank

   A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment. The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand. How should the company deploy the model into production to meet these requirements?

   • ACreate a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
   • BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
   • CInstall SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
   • DUse Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.

   Show answer

   Answer: A

   References

   • AWS documentation

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3. Question 43 · 226 in bank

   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?

   • ARun the primary node, core nodes, and task nodes on On-Demand Instances.
   • BRun the primary node, core nodes, and task nodes on Spot Instances.
   • CRun the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
   • DRun the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.

   Show answer

   Answer: D

   Explanation

   https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html#emr-plan-spot-instances "The task nodes process data but do not hold persistent data in HDFS. If they terminate because the Spot price has risen above your maximum Spot price, no data is lost"

   Source: Adapted from community discussion — GiorgioGss

   References

   • AWS documentation

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4. Question 44 · 226 in bank

   Select 2

   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.)

   • AUse Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
   • BUse Amazon SageMaker Ground Truth for data labeling.
   • CDeploy models by using AWS Lambda functions.
   • DUse AWS Trainium instances for training.
   • EUse PyTorch or TensorFlow with the distributed training option.

   Show answer

   Answer: AD

   References

   • AWS documentation
   • AWS documentation

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5. Question 45 · 226 in bank

   Case Study - A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application. Which action will meet this requirement?

   • AConfigure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
   • BInvoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
   • CUse AWS Glue Data Quality to monitor bias.
   • DUse SageMaker notebooks to compare the bias.

   Show answer

   Answer: A

   Explanation

   A. Yes, Clarify allows to get bias - https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-configure-processing-jobs.html B. No, the built-in image sagemaker-model-monitor-analyzer provides a range of model monitoring capabilities (constraint suggestion, statistics generation, constraint validation against a baseline, and emitting Amazon CloudWatch metrics) but you need Clarify for bias C. No, Glue Data Quality doesn't analyze bias D. No, well from a Notebook you can execute pretty much everything including a Clarify Job, however notebooks are for experiments and models development not for enabling real-time application features

   Source: Adapted from community discussion — ninomfr64

   References

   • AWS documentation
   • AWS documentation

   Next question

6. Question 46 · 226 in bank

   A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ТВ in size and consists of CSV, JSON, Apache Parquet, and simple text files. The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated. Which solution will meet these requirements?

   • AProcess data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
   • BUse Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
   • CProcess data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
   • DUse Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.

   Show answer

   Answer: D

   Explanation

   Large datasets + Multiple file formats + Complex automation & orchestration of ML workflows + NLP Transformation ---> Sagemaker pipelines + Event bridge for trigger

   Source: Adapted from community discussion — Saransundar

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7. Question 47 · 226 in bank

   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
   • BAWS::SageMaker::Endpoint
   • CAWS::SageMaker::NotebookInstance
   • DAWS::SageMaker::Pipeline

   Show answer

   Answer: A

   Explanation

   Type: AWS::SageMaker::Model Properties: Containers: - ContainerDefinition EnableNetworkIsolation: Boolean ExecutionRoleArn: String InferenceExecutionConfig: InferenceExecutionConfig ModelName: String PrimaryContainer: ContainerDefinition Tags: - Tag VpcConfig: VpcConfig

   Source: Adapted from community discussion — Saransundar

   References

   • AWS documentation

   Next question

8. Question 48 · 226 in bank

   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.
   • DConfigure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.

   Show answer

   Answer: C

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9. Question 49 · 226 in bank

   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

   Show answer

   Answer: D

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10. Question 50 · 226 in bank

    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

    Show answer

    Answer: D

    Explanation

    A. Low precision: Increases false positives; less relevant here. B. High precision: Reduces false positives; not the priority. C. Low recall: Increases false negatives; must be avoided. D. High recall: Correct; minimizes false negatives.

    Source: Adapted from community discussion — Saransundar

    References

    • AWS documentation

    Next question

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MLA-C01 practice, question 41. 226 questions available.