MLA-C01 exam dumps

MLA-C01 practice question 317 of 458

AWS Certified Machine Learning Engineer - Associate. Associate level, Amazon Web Services. Free question with the correct answer and a full explanation.

MLA-C01 Question 317

Select 2

You are deploying a machine learning model using Amazon SageMaker, and your model needs to handle fluctuating traffic patterns. During peak hours, the endpoint should scale up to manage high demand, while during off-peak hours, the endpoint should scale down to save costs. Which steps should you take to implement an auto-scaling policy for your SageMaker endpoint?

  1. A

    Create an Amazon CloudWatch alarm to monitor a metric, such as InvocationsPerInstance, and link it to an auto-scaling policy.

  2. B

    Configure the SageMaker endpoint to automatically scale horizontally without any additional configuration.

  3. C

    Set up a target tracking scaling policy in Application Auto Scaling for the SageMaker endpoint.

  4. D

    Manually adjust the number of instances for the SageMaker endpoint based on daily traffic patterns.

  5. E

    Define a desired instance count and associate it with the auto-scaling policy to maintain the required capacity.

Show answer and explanation

Correct answers: A, C

Explanation

To meet scalability requirements and handle fluctuating traffic, you can use SageMaker's integration with Application Auto Scaling. This involves setting up a CloudWatch alarm to monitor metrics, such as InvocationsPerInstance, and configuring a target tracking scaling policy. These mechanisms ensure that the endpoint scales up during high demand and scales down during low demand, optimizing both performance and cost.

  • A. Correct.

    Correct: Amazon CloudWatch alarms can be used to monitor metrics such as InvocationsPerInstance, which can trigger scaling actions when thresholds are breached.

  • B. Incorrect.

    Incorrect: SageMaker endpoints do not scale automatically without explicit configuration of auto-scaling policies.

  • C. Correct.

    Correct: Target tracking scaling policies in Application Auto Scaling allow you to automatically scale SageMaker endpoints based on predefined metrics like average invocations per instance.

  • D. Incorrect.

    Incorrect: Manually adjusting instance counts is not an automated solution and does not leverage SageMaker's auto-scaling capabilities.

  • E. Incorrect.

    Incorrect: Defining a desired instance count does not enable auto-scaling; instead, it sets a static number of instances.

Timed practice exam

Take a MLA-C01 practice test under exam conditions

65 questions in 130 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam