AWS AIF-C01 Free Practice Questions — Page 3

Q: A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams. Which SageMaker feature meets these requirements?

A. Amazon SageMaker Feature Store. Amazon SageMaker Feature Store is designed to store share and manage ML features across teams. It provides a centralized repository for features that can be accessed by multiple teams ensuring consistency and collaboration. Data Wrangler is for data preparation. Clarify is for bias detection and explainability. Model Cards document model information but do not manage shared variables.

Q: A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards the VPC is not allowed access to any internet traffic. Which AWS service or feature will meet these requirements?

A. AWS PrivateLink. AWS PrivateLink allows private connectivity between VPCs and AWS services without exposing traffic to the public internet. This enables the financial institution to access Amazon Bedrock from within their VPC without internet access meeting regulatory compliance requirements. Macie is for data security not network connectivity. CloudFront is a CDN that uses the internet. An internet gateway would provide internet access which violates the requirement.

Q: Which metric measures the runtime efficiency of operating AI models?

C. Average response time. Average response time measures the runtime efficiency of AI models during inference. It indicates how quickly the model produces outputs which is critical for operational efficiency. CSAT measures customer satisfaction not runtime efficiency. Training time per epoch measures training efficiency not operational runtime. Number of training instances relates to data volume not runtime performance.

Question 1

A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams. Which SageMaker feature meets these requirements?

Answer

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Correct Answer: A. Amazon SageMaker Feature Store

Amazon SageMaker Feature Store is designed to store share and manage ML features across teams. It provides a centralized repository for features that can be accessed by multiple teams ensuring consistency and collaboration. Data Wrangler is for data preparation. Clarify is for bias detection and explainability. Model Cards document model information but do not manage shared variables.

Question 2

A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer. What can Amazon Q Developer do to help the company meet these requirements?

Answer

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Correct Answer: A. Create software snippets and reference tracking and open source license tracking.

Amazon Q Developer helps increase developer productivity by generating software code snippets providing reference tracking and tracking open source licenses. These features help developers write code faster while maintaining compliance. Running applications serverlessly is AWS Lambda functionality. Voice commands and audio conversion are not primary features of Amazon Q Developer.

Question 3

A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards the VPC is not allowed access to any internet traffic. Which AWS service or feature will meet these requirements?

Answer

Show Answer & Explanation

Correct Answer: A. AWS PrivateLink

AWS PrivateLink allows private connectivity between VPCs and AWS services without exposing traffic to the public internet. This enables the financial institution to access Amazon Bedrock from within their VPC without internet access meeting regulatory compliance requirements. Macie is for data security not network connectivity. CloudFront is a CDN that uses the internet. An internet gateway would provide internet access which violates the requirement.

Question 4

A company wants to develop an educational game where users answer questions such as the following: A jar contains six red four green and three yellow marbles. What is the probability of choosing a green marble from the jar? Which solution meets these requirements with the LEAST operational overhead?

Answer

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Correct Answer: C. Use code that will calculate probability by using simple rules and computations.

Simple probability calculations like selecting marbles from a jar follow basic mathematical formulas. Using code with simple rules and computations has the least operational overhead because it requires no ML model training maintenance or infrastructure. This is a deterministic problem with a known formula. ML approaches would add unnecessary complexity and overhead for such straightforward calculations.

Question 5

Which metric measures the runtime efficiency of operating AI models?

Answer

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Correct Answer: C. Average response time

Average response time measures the runtime efficiency of AI models during inference. It indicates how quickly the model produces outputs which is critical for operational efficiency. CSAT measures customer satisfaction not runtime efficiency. Training time per epoch measures training efficiency not operational runtime. Number of training instances relates to data volume not runtime performance.

AWS AIF-C01 Free Practice Questions — Page 3

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