Amazon MLA-C01 Exam Questions - Real Practice Questions for Guaranteed Success

Question 1

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

AUse Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

BUse Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

CUse AWS Glue DataBrew built-in features to oversample the minority class.

DUse the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Correct : D

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AUse Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

BUse Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

CUse AWS Glue DataBrew built-in features to oversample the minority class.

DUse the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

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Question 2

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.

Correct : B

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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.

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Question 3

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 : D

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ALow precision

BHigh precision

CLow recall

DHigh recall

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Question 4

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.

Correct : B

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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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Question 5

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.

Correct : A

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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.

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