MLA-C01 — questions

Page 5 of 12 · 226 total questions.

MLA-C01 Question 81

Topic 1 · Question 81

A company has a binary classification model in production. An ML engineer needs to develop a new version of the model. The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements. Which metric should the ML engineer use for the model recalibration?

AAccuracy (correct answer)
BPrecision
CRecall
DSpecificity

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Accuracy.

MLA-C01 Question 82

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Topic 1 · Question 82

A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications. Which solution will meet these requirements?

AUse AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
BUse AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
CUse SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications. (correct answer)
DUse SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

Explanation

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

MLA-C01 Question 83

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Topic 1 · Question 83

A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts. An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources. Which solution will meet these requirements with the LEAST development effort?

ACreate code to evaluate each instance's memory and compute usage.
BAdd cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
CCheck AWS CloudTrail event history for the creation of the resources.
DRun AWS Compute Optimizer. (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: Run AWS Compute Optimizer.

Explanation

AWS Compute Optimizer recommends right-sizing based on utilization.

MLA-C01 Question 84

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Topic 1 · Question 84

A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories. Which solution will meet these requirements?

AConfigure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
BCreate a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog.
CUse the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts. (correct answer)
DUse an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-ac...

Explanation

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

MLA-C01 Question 85

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Topic 1 · Question 85

A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model. Which solution will set up the required online validation with the LEAST operational overhead?

AUse production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch. (correct answer)
BUse production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
CCreate a new SageMaker endpoint. Use production variants to add the new model to the new endpoint. Monitor the number of invocations by using Amazon CloudWatch.
DConfigure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint. Monitor the number of invocations by using AWS CloudTrail.

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.

Explanation

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 86

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Topic 1 · Question 86

A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item. Which Amazon SageMaker algorithm will meet these requirements?

AImage classification
BXGBoost
CObject detection (correct answer)
DK-nearest neighbors (k-NN)

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Object detection.

MLA-C01 Question 87

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Topic 1 · Question 87

A company has an Amazon S3 bucket that contains 1 ТВ of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet. An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data. The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future. Which solution will meet these requirements?

AUse DataBrew to process the existing S3 folder. Store the output in Apache Parquet format. (correct answer)
BUse DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.
CSeparate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in Apache Parquet format.
DSeparate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in AWS Glue Parquet format.

Reveal answer & explanation

Correct answer: A

The correct answer is A. Option A: Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed.

MLA-C01 Question 88

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Topic 1 · Question 88 · Select all that apply

A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the products into the two categories by using the model to analyze photos on the assembly line. Which metrics should the company use to evaluate the model's performance? (Choose two.)

APrecision and recall
BRoot mean square error (RMSE) and mean absolute percentage error (MAPE)
CAccuracy and F1 score (correct answer)
DBilingual Evaluation Understudy (BLEU) score
EPerplexity

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Accuracy and F1 score.

MLA-C01 Question 89

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Topic 1 · Question 89

An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job. Which solution will meet these requirements?

AEncrypt communication between nodes for batch processing.
BEncrypt communication between nodes in a training cluster. (correct answer)
CSpecify an AWS Key Management Service (AWS KMS) key during creation of the training job request.
DSpecify an AWS Key Management Service (AWS KMS) key during creation of the SageMaker domain.

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Encrypt communication between nodes in a training cluster.

MLA-C01 Question 90

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Topic 1 · Question 90 · Select all that apply

An ML engineer needs to use metrics to assess the quality of a time-series forecasting model. Which metrics apply to this model? (Choose two.)

ARecall
BLogLoss
CRoot mean square error (RMSE) (correct answer)
DInferenceLatency
EAverage weighted quantile loss (wQL) (correct answer)

Reveal answer & explanation

Correct answer: C, E

The correct answer is C, E. Option C: Root mean square error (RMSE) Option E: Average weighted quantile loss (wQL).

MLA-C01 Question 91

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Topic 1 · Question 91

A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must not allow a cold start for the models. Which solution will meet these requirements?

ACreate a SageMaker Serverless Inference endpoint for each model. Use provisioned concurrency for the endpoints.
BCreate a SageMaker Asynchronous Inference endpoint for each model. Create an auto scaling policy for each endpoint.
CCreate a SageMaker endpoint. Create an inference component for each model. In the inference component settings, specify the newly created endpoint. Create an auto scaling policy for each inference component. Set the parameter for the minimum number of copies to at least 1. (correct answer)
DCreate an Amazon S3 bucket. Store all the model artifacts in the S3 bucket. Create a SageMaker multi-model endpoint. Point the endpoint to the S3 bucket. Create an auto scaling policy for the endpoint. Set the parameter for the minimum number of copies to at least 1.

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Create a SageMaker endpoint. Create an inference component for each model. In the inference component settings, specify the newly created endpoint. Create an auto scaling policy for each inference component. Set the p...

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. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 92

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Topic 1 · Question 92

A company runs training jobs on Amazon SageMaker by using a compute optimized instance. Demand for training runs will remain constant for the next 55 weeks. The instance needs to run for 35 hours each week. The company needs to reduce its model training costs. Which solution will meet these requirements?

AUse a serverless endpoint with a provisioned concurrency of 35 hours for each week. Run the training on the endpoint.
BUse SageMaker Edge Manager for the training. Specify the instance requirement in the edge device configuration. Run the training.
CUse the heterogeneous cluster feature of SageMaker Training. Configure the instance_type, instance_count, and instance_groups arguments to run training jobs.
DOpt in to a SageMaker Savings Plan with a 1-year term and an All Upfront payment. Run a SageMaker Training job on the instance. (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: Opt in to a SageMaker Savings Plan with a 1-year term and an All Upfront payment. Run a SageMaker Training job on the instance.

Explanation

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

MLA-C01 Question 93

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Topic 1 · Question 93

A company has an ML model that uses historical transaction data to predict customer behavior. An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's predictive accuracy. The ML engineer must examine the input data and the resulting predictions to identify trends that could skew the model's performance across different demographics. Which solution will provide this level of analysis?

AUse Amazon CloudWatch to monitor network metrics and CPU metrics for resource optimization during model training.
BCreate AWS Glue DataBrew recipes to correct the data based on statistics from the model output.
CUse SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy. (correct answer)
DCreate AWS Lambda functions to automate data pre-processing and to ensure consistent quality of input data for the model.

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Use SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy.

Explanation

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

MLA-C01 Question 94

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Topic 1 · Question 94

A company uses 10 Reserved Instances of accelerated instance types to serve the current version of an ML model. An ML engineer needs to deploy a new version of the model to an Amazon SageMaker real-time inference endpoint. The solution must use the original 10 instances to serve both versions of the model. The solution also must include one additional Reserved Instance that is available to use in the deployment process. The transition between versions must occur with no downtime or service interruptions. Which solution will meet these requirements?

AConfigure a blue/green deployment with all-at-once traffic shifting.
BConfigure a blue/green deployment with canary traffic shifting and a size of 10%. (correct answer)
CConfigure a shadow test with a traffic sampling percentage of 10%.
DConfigure a rolling deployment with a rolling batch size of 1.

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Configure a blue/green deployment with canary traffic shifting and a size of 10%.

Explanation

AWS Config tracks resource configuration changes and evaluates compliance. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 95

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Topic 1 · Question 95

An IoT company uses Amazon SageMaker to train and test an XGBoost model for object detection. ML engineers need to monitor performance metrics when they train the model with variants in hyperparameters. The ML engineers also need to send Short Message Service (SMS) text messages after training is complete. Which solution will meet these requirements?

AUse Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.
BUse Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery. (correct answer)
CUse AWS CloudTrail to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.
DUse AWS CloudTrail to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.

Explanation

Amazon SNS is a managed pub/sub service for fan-out notifications to many subscribers. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health.

MLA-C01 Question 96

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Topic 1 · Question 96

A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet. Which solution will meet this requirement?

ARun the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
BRun the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint. (correct answer)
CRun the SageMaker training jobs in public subnets that have an attached security group. In the security group, use inbound rules to limit traffic from the internet. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
DEncrypt traffic to Amazon S3 by using a bucket policy that includes a value of True for the aws:SecureTransport condition key. Use default at-rest encryption for Amazon S3. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. A gateway VPC endpoint gives private, no-cost access to Amazon S3 and DynamoDB from within the VPC. A VPC endpoint provides private connectivity to AWS services without traversing the public internet.

MLA-C01 Question 97

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Topic 1 · Question 97

A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set of documents in an Amazon S3 bucket. The documents consist of .csv files and .docx files. Which solution will meet these requirements with the LEAST operational overhead?

ACreate a pipeline in Amazon SageMaker Pipelines to generate a new model. Call the new model from Amazon Bedrock to perform RAG queries.
BConvert the data into vectors. Store the data in an Amazon Neptune database. Connect the database to Amazon Bedrock. Call the Amazon Bedrock API to perform RAG queries.
CFine-tune an existing LLM by using an AutoML job in Amazon SageMaker. Configure the S3 bucket as a data source for the AutoML job. Deploy the LLM to a SageMaker endpoint. Use the endpoint to perform RAG queries.
DCreate a knowledge base for Amazon Bedrock. Configure a data source that references the S3 bucket. Use the Amazon Bedrock API to perform RAG queries. (correct answer)

Reveal answer & explanation

Correct answer: D

The correct answer is D. Option D: Create a knowledge base for Amazon Bedrock. Configure a data source that references the S3 bucket. Use the Amazon Bedrock API to perform RAG queries.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Amazon Bedrock provides access to foundation models through a managed API. AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 98

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Topic 1 · Question 98

A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference requests must be processed in 60 minutes or less. Which SageMaker inference option will meet these requirements?

AServerless inference
BAsynchronous inference (correct answer)
CReal-time inference
DBatch transform

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Asynchronous inference.

MLA-C01 Question 99

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Topic 1 · Question 99

An ML engineer notices class imbalance in an image classification training job. What should the ML engineer do to resolve this issue?

AReduce the size of the dataset.
BTransform some of the images in the dataset.
CApply random oversampling on the dataset. (correct answer)
DApply random data splitting on the dataset.

Reveal answer & explanation

Correct answer: C

The correct answer is C. Option C: Apply random oversampling on the dataset.

MLA-C01 Question 100

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Topic 1 · Question 100

A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer needs to implement a solution to mask credit card numbers in the files before the model is retrained. Which solution will meet this requirement with the LEAST development effort?

ACreate a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
BCreate Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data. (correct answer)
CCreate Apache Spark code to run on an AWS Glue job. Program the code to perform a regex operation to find and mask sensitive data.
DCreate Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an operation to find and mask sensitive data.

Reveal answer & explanation

Correct answer: B

The correct answer is B. Option B: Create Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data.

Explanation

AWS Glue is a serverless ETL and data-catalog service that prepares and integrates data. This option needs the least custom development effort.

Showing questions 81–100 of 226 · Page 5 of 12