MLA-C01 practice questions 31–40

1. Question 31 · 226 in bank

   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?

   • ADeploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
   • BDeploy 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.
   • CUse Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
   • DUse Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

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   Answer: C

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2. Question 32 · 226 in bank

   An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets. Which solution will meet these requirements?

   • AUse Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
   • BUse AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
   • CUse Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
   • DUse Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

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   Answer: B

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3. Question 33 · 226 in bank

   A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry. The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings. Which solution will meet these requirements?

   • ACreate a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
   • BCreate a model group for each category. Move the existing models into these category model groups.
   • CUse SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
   • DCreate a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

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   Answer: D

   Explanation

   A could also be a valid option but in here we see exactly this: https://docs.aws.amazon.com/sagemaker/latest/dg/modelcollections.html "Any operation you perform on your Collections does not affect the integrity of the individual Model Groups they contain—the underlying Model Group artifacts in Amazon S3 and Amazon ECR are not modified."

   Source: Adapted from community discussion — GiorgioGss

   References

   • AWS documentation

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4. Question 34 · 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 must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints. Which solution will meet this requirement?

   • AUse SageMaker Experiments to facilitate the approval process during model registration.
   • BUse SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
   • CUse SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
   • DUse SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

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   Answer: D

   References

   • AWS documentation

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5. Question 35 · 226 in bank

   A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain. Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address. Which update to the network configuration will meet this requirement?

   • ACreate a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
   • BCreate a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
   • CCreate a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
   • DCreate a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

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   Answer: B

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6. Question 36 · 226 in bank

   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?

   • ATrain and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
   • BUse Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
   • CUse Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
   • DUse Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.

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   Answer: B

   Explanation

   LEAST amount of time -> A is out C is out because Claude does NOT fit for summarization D is out because that's for image generation

   Source: Adapted from community discussion — GiorgioGss

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7. Question 37 · 226 in bank

   A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second. The company needs to implement a scalable solution on AWS to identify anomalous data points. Which solution will meet these requirements with the LEAST operational overhead?

   • AIngest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.
   • BIngest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
   • CIngest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
   • DSend real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.

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   Answer: A

   References

   • AWS documentation

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8. Question 38 · 226 in bank

   A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product. Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

   • AUse Amazon Rekognition to analyze sentiments of the chat conversations.
   • BTrain a Naive Bayes classifier to analyze sentiments of the chat conversations.
   • CUse Amazon Comprehend to analyze sentiments of the chat conversations.
   • DUse random forests to classify sentiments of the chat conversations.

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   Answer: C

   References

   • AWS documentation

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9. Question 39 · 226 in bank

   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?

   • AIncrease the temperature parameter and the top_k parameter.
   • BIncrease the temperature parameter. Decrease the top_k parameter.
   • CDecrease the temperature parameter. Increase the top_k parameter.
   • DDecrease the temperature parameter and the top_k parameter.

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   Answer: D

   References

   • AWS documentation

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10. Question 40 · 226 in bank

    A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter. What should the company do to MINIMIZE false positives?

    • ASet the value of the weight decay hyperparameter to zero.
    • BIncrease the number of training epochs.
    • CIncrease the value of the target_precision hyperparameter.
    • DChange the value of the predictorjype hyperparameter to regressor.

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    Answer: C

    Explanation

    A. Weight decay = 0 → No regularization, doesn’t target false positives. B. More epochs → Longer training, risks overfitting, no direct impact on false positives. C. Higher precision → Prioritizes correct positives, reduces false positives. D. Regressor → Predicts continuous values, unsuitable for classification.

    Source: Adapted from community discussion — Saransundar

    References

    • AWS documentation

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MLA-C01 practice, question 31. 226 questions available.