🔍

MLA-C01 — questions

Page 12 of 12 · 226 total questions.

MLA-C01 Question 231

Single page →
Topic 1 · Question 231

An ML engineer uses A/B testing to dynamically select recommendation models. The models are deployed on Amazon SageMaker AI endpoints. The ML engineer needs to monitor system metrics such as latency, call volume, and HTTP status codes when the endpoints are invoked. Which solution will meet these requirements with the LEAST operational overhead?

  • AUse AWS X-Ray tracing to monitor SageMaker AI endpoints.
  • BConfigure Amazon CloudWatch dashboards with AWS Lambda log processors. (correct answer)
  • CEnable AWS Identity and Access Management (IAM) Access Analyzer to track SageMaker AI endpoint metrics.
  • DDeploy AWS Trusted Advisor checks on SageMaker AI endpoint instances.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Configure Amazon CloudWatch dashboards with AWS Lambda log processors.

Explanation

AWS Lambda runs code without provisioning servers and scales automatically, which minimizes operational overhead. Amazon RDS is a managed relational database that handles patching, backups and failover. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health.

MLA-C01 Question 232

Single page →
Topic 1 · Question 232 · Select all that apply

An ML engineer is importing a custom model from the Hugging Face Hub into Amazon Bedrock. The ML engineer wants to use the model with the Amazon Bedrock API in an agentic AI application. Which combination of steps will meet these requirements? (Choose two.)

  • AImport the model for deployment by using Amazon Bedrock On-Demand mode. (correct answer)
  • BConvert the model to a quantized format. Import the quantized model after conversion.
  • CImport the model directly by using the Amazon SageMaker AI SDK in the agentic AI application.
  • DDuring the model import process, configure an auto-scaling policy before using the model in the agentic AI application.
  • EIn the import job, provide the complete Amazon S3 URI that points to the Hugging Face model files. Ensure that the S3 URI is in the same AWS account as the Amazon Bedrock import job. (correct answer)
Reveal answer & explanation
Correct answer: A, E

The correct answer is A, E. Option A: Import the model for deployment by using Amazon Bedrock On-Demand mode. Option E: In the import job, provide the complete Amazon S3 URI that points to the Hugging Face model files. Ensure that the S3 URI is in the same AWS account as the Amazon Bedrock import job.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Amazon Bedrock provides access to foundation models through a managed API.

MLA-C01 Question 233

Single page →
Topic 1 · Question 233

A company is developing ML models by using PyTorch and TensorFlow estimators with Amazon SageMaker AI. An ML engineer configures the SageMaker AI estimator and now needs to initiate a training job that uses a training dataset. Which SageMaker AI SDK method can initiate the training job?

  • Afit method (correct answer)
  • Bcreate_model method
  • Cdeploy method
  • Dpredict method
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: fit method.

MLA-C01 Question 234

Single page →
Topic 1 · Question 234

An ML engineer used Amazon SageMaker Studio to train a neural network. The neural network logs its information into TensorBoard and uses stochastic gradient descent (SGD) as the optimizer. The ML engineer reviewed training graphs and discovered that the accuracy was not increasing and the loss was decreasing very slowly. The ML engineer needs to improve the model’s performance without increasing the total training time. Which solution will meet these requirements?

  • AIncrease the initial learning rate. (correct answer)
  • BDecrease the initial learning rate.
  • CIncrease the total number of epochs.
  • DDecrease the total number of epochs.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Increase the initial learning rate.

MLA-C01 Question 235

Single page →
Topic 1 · Question 235

A company wants to use large language models (LLMs) that are supported by Amazon Bedrock to develop a chat interface for the company’s internal technical documentation. The company stores the documentation as dozens of text files that are several megabytes in total size. The company updates the text files often. Which solution will meet these requirements MOST cost-effectively?

  • ACreate a new LLM on Amazon Bedrock. Train the new LLM on the original dataset and the company documentation. Make the new model available in Bedrock for calls from the chat interface.
  • BIntegrate the company documentation with Amazon Bedrock guardrails. Invoke the guardrails for all Amazon Bedrock calls from the chat interface.
  • CUse all the text files to fine tune a model in Amazon Bedrock. Use the fine-tuned model to process user prompts.
  • DUpload all the text files to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when the chat interface makes calls to Amazon Bedrock. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Upload all the text files to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when the chat interface makes calls to Amazon Bedrock.

Explanation

Amazon Bedrock provides access to foundation models through a managed API. This option delivers the requirement at the lowest cost.

MLA-C01 Question 236

Single page →
Topic 1 · Question 236

A company uses an ML model to recommend videos to users. The model is deployed on Amazon SageMaker AI. The model performed well initially after deployment, but the model’s performance has degraded over time. Which solution can the company use to identify model drift in the future?

  • ACreate a monitoring job in SageMaker Model Monitor. Then create a baseline from the training dataset.
  • BCreate a baseline from the training dataset. Then create a monitoring job in SageMaker Model Monitor. (correct answer)
  • CCreate a baseline by using a built-in rule in SageMaker Clarify. Monitor the drift in Amazon CloudWatch.
  • DRetrain the model on new data. Compare the retrained model's performance to the original model’s performance.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create a baseline from the training dataset. Then create a monitoring job in SageMaker Model Monitor.

Explanation

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

Showing questions 221–226 of 226 · Page 12 of 12