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MLA-C01 exam dumps: questions 301 to 400 of 457

Page 4 of the free MLA-C01 question bank for the AWS Certified Machine Learning Engineer - Associate exam. Questions 301 to 400 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated December 2024

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Questions 301 to 305 of 457

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MLA-C01 Question 301

Select 3

You are deploying a machine learning model on Amazon SageMaker and need to ensure the endpoint scales automatically to handle fluctuating traffic. You are considering two scaling policies: target tracking scaling and step scaling. Which of the following statements correctly describes the differences between these scaling policies?

A
Target tracking scaling adjusts capacity to maintain a predefined metric, such as average CPU utilization or request count per instance.
B
Step scaling requires predefined thresholds and adjusts capacity by a fixed number of instances when those thresholds are breached.
C
Target tracking scaling is better suited for handling unpredictable, spiky traffic patterns compared to step scaling.
D
Step scaling automatically creates CloudWatch alarms based on the target metric chosen.
E
Target tracking scaling is more complex to configure than step scaling.

Show answer and explanation

Correct answers: A, B, C

Explanation

Both target tracking scaling and step scaling are valid options for automatic scaling in AWS. However, they differ in their configuration and use cases. Target tracking scaling adjusts capacity dynamically to maintain a target metric, making it suitable for variable or unpredictable traffic patterns. Step scaling, on the other hand, uses predefined thresholds and discrete scaling actions, which may be less adaptive for spiky traffic but offers precise control over scaling behavior. Understanding these differences is key to selecting the right scaling policy for your machine learning endpoint.

A. Correct.
This statement is correct. Target tracking scaling is designed to maintain a predefined metric value by adjusting capacity dynamically.
B. Correct.
This statement is correct. Step scaling operates based on predefined thresholds and makes discrete adjustments when those thresholds are crossed.
C. Correct.
This statement is correct. Target tracking scaling is more adaptive and better suited for handling spiky traffic patterns because it adjusts capacity continuously to maintain the target metric.
D. Incorrect.
This statement is incorrect. CloudWatch alarms are not automatically created for step scaling; they need to be explicitly defined and configured.
E. Incorrect.
This statement is incorrect. Target tracking scaling is simpler to configure as it only requires setting a target metric and desired value, whereas step scaling requires defining multiple thresholds and scaling actions.

MLA-C01 Question 302

Single answer

You are managing an Amazon SageMaker endpoint for a real-time machine learning model with unpredictable traffic patterns. You need to select a scaling policy to ensure optimal cost efficiency and performance. Which scaling policy should you choose, and why?

A
Target tracking scaling policy
B
Step scaling policy
C
Scheduled scaling policy
D
Manual scaling

Show answer and explanation

Correct answer: A

Explanation

For endpoints with unpredictable traffic patterns, target tracking scaling is the most effective policy. It dynamically adjusts based on a target metric, such as latency or CPU utilization, ensuring the system performs well while remaining cost-efficient. Other policies, like step scaling or scheduled scaling, are either too rigid or suited for predictable workloads, and manual scaling is impractical for real-time adjustments.

A. Correct.
Target tracking scaling policy is ideal for unpredictable traffic because it automatically adjusts the capacity of the endpoint to maintain a target metric, such as latency or utilization. This eliminates the need for manual intervention and ensures a balance between cost and performance.
B. Incorrect.
Step scaling policy is better suited for scenarios where specific, predefined thresholds trigger scaling actions. It is less effective for unpredictable traffic patterns because it requires detailed configuration and does not dynamically adjust to changing patterns.
C. Incorrect.
Scheduled scaling policy is designed for predictable traffic patterns where scaling actions can be planned ahead of time based on known usage patterns. It is not suitable for traffic that is highly unpredictable.
D. Incorrect.
Manual scaling requires human intervention and is not a scalable or cost-efficient solution for managing unpredictable traffic patterns. It does not provide the automation required for real-time adjustment.

MLA-C01 Question 303

Single answer

You are building a machine learning inference application hosted on EC2 Spot Instances behind an Auto Scaling group. The application experiences spikes in traffic during business hours, and maintaining low latency is critical. You need to decide between a target tracking scaling policy and a step scaling policy for the Auto Scaling group. Which scaling policy is most appropriate, and why?

A
Use a target tracking scaling policy to automatically adjust the number of instances based on a defined metric, such as average CPU utilization, to maintain consistent performance.
B
Use a step scaling policy to define specific scaling steps based on distinct metric thresholds, allowing fine-grained control over scaling behavior.
C
Use a step scaling policy because it is designed to handle sudden traffic spikes more effectively than target tracking scaling.
D
Use a target tracking scaling policy because it allows you to define multiple scaling steps for different traffic levels.

Show answer and explanation

Correct answer: A

Explanation

A target tracking scaling policy is better suited for applications with unpredictable traffic patterns and critical latency requirements. It dynamically adjusts the number of instances based on a target metric, such as average CPU utilization, ensuring consistent performance without requiring manual configuration of thresholds. In contrast, step scaling policies are more rigid and require predefined thresholds, making them less effective for highly variable traffic scenarios.

A. Correct.
Correct: A target tracking scaling policy automatically adjusts the number of instances to maintain a target value for a defined metric, such as average CPU utilization. This is ideal for applications with variable traffic patterns and low-latency requirements, as it simplifies scaling decisions.
B. Incorrect.
Incorrect: While step scaling policies allow fine-grained control, they are not as dynamic as target tracking policies for handling variable traffic patterns. They require pre-defined thresholds, which may not adapt well to unexpected traffic changes.
C. Incorrect.
Incorrect: Step scaling policies are not inherently better at handling sudden traffic spikes compared to target tracking. Target tracking can respond dynamically to metric changes without manual thresholds.
D. Incorrect.
Incorrect: Target tracking policies do not allow multiple scaling steps but instead focus on maintaining a target metric. This statement conflates the features of target tracking and step scaling policies.

MLA-C01 Question 304

Single answer

You are tasked with deploying a machine learning infrastructure on AWS that requires flexibility in defining resources using Python and the ability to integrate with existing CI/CD pipelines. The infrastructure may also need frequent updates as new requirements emerge. Which AWS IaC option is most suitable for this scenario?

A
AWS CloudFormation
B
AWS Cloud Development Kit (AWS CDK)
C
AWS Elastic Beanstalk
D
AWS OpsWorks

Show answer and explanation

Correct answer: B

Explanation

AWS CDK is the most suitable choice for this scenario because it allows users to define infrastructure as code using high-level programming languages such as Python, which provides flexibility and programmability. This makes it easy to integrate with CI/CD pipelines and adapt to frequent updates. CloudFormation, while powerful, is more suitable for static, declarative definitions, and the other options (Elastic Beanstalk and OpsWorks) do not meet the specific requirements of defining resources programmatically.

A. Incorrect.
AWS CloudFormation is a declarative IaC tool that allows you to define resources using JSON or YAML. While it is powerful for managing infrastructure, it lacks the flexibility of using high-level programming languages like Python, which is needed in this scenario.
B. Correct.
AWS Cloud Development Kit (AWS CDK) is the correct option because it allows you to define AWS infrastructure using familiar programming languages like Python, which makes it suitable for integrating with CI/CD pipelines and adapting to frequent updates.
C. Incorrect.
AWS Elastic Beanstalk is not an IaC tool. It is a platform-as-a-service (PaaS) solution for deploying and managing applications, and it does not provide the flexibility required for defining custom infrastructure using Python.
D. Incorrect.
AWS OpsWorks is primarily used for configuration management and automating server configurations using Chef or Puppet. It is not designed for defining AWS resources programmatically using programming languages like Python.

MLA-C01 Question 305

Select 2

You are working on an AWS Machine Learning project and need to provision and manage the infrastructure for both development and production environments. The development environment requires rapid iteration with less focus on stability, while the production environment demands highly stable and repeatable deployments. Which combination of AWS Infrastructure as Code (IaC) options would best suit these requirements?

A
Use AWS CloudFormation for both development and production environments to ensure consistency across deployments.
B
Use AWS CDK for the development environment for flexibility and faster iteration, and AWS CloudFormation for the production environment for stability and repeatability.
C
Use AWS CDK for the production environment to enable easier code-based modifications and AWS CloudFormation for development to ensure consistency during testing.
D
Use AWS CloudFormation for the production environment to leverage declarative templates and AWS CDK for the development environment for a more programmatic approach.

Show answer and explanation

Correct answers: B, D

Explanation

AWS CDK and AWS CloudFormation serve different purposes in an Infrastructure as Code (IaC) workflow. AWS CDK provides a programmatic approach, making it ideal for development environments where rapid changes and iterations are required. On the other hand, AWS CloudFormation uses declarative templates, ensuring stable, repeatable, and predictable deployments, making it the preferred choice for production environments. Combining these two tools allows you to optimize for both flexibility during development and stability during production.

A. Incorrect.
AWS CloudFormation is excellent for ensuring stability and repeatability but is not ideal for rapid iteration in the development environment. Using it for both environments may slow down development.
B. Correct.
This is a valid combination. AWS CDK offers a programmatic approach and flexibility, ideal for rapid iteration during development, while AWS CloudFormation provides stability and repeatability, which are critical for production deployments.
C. Incorrect.
Using AWS CDK for the production environment is not ideal because it introduces potential risks of instability due to its programmatic nature, which can lead to less predictable deployments.
D. Correct.
This is a valid combination. AWS CloudFormation's declarative templates are well-suited for production environments requiring stability, while AWS CDK's programmatic approach is better for development environments where flexibility is needed.

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MLA-C01 practice questions 301 to 400 of 457

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