AIF-C01 Practice Test 10 — 10 Questions

Question 1

A company uses Amazon SageMaker to build and deploy models. They want to enforce compliance that all model endpoints are encrypted in transit and use least privilege access. Which THREE steps should they take? (Choose THREE.)

Accepted Answer

Configure the SageMaker endpoint to use a custom SSL certificate via AWS Certificate Manager. Option A is correct because configuring a SageMaker endpoint to use a custom SSL certificate from AWS Certificate Manager (ACM) ensures that all data transmitted between clients and the endpoint is encrypted in transit using TLS. This enforces the compliance requirement for encryption in transit by replacing the default SageMaker certificate with a customer-managed certificate, which can be validated and rotated as needed.

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Enable AWS CloudTrail to log all endpoint invocations

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Disable root access on the SageMaker notebook instances

Question 2

A company is using Amazon SageMaker to train machine learning models on sensitive customer data. Which AWS service can be used to encrypt the data at rest in the S3 bucket used by SageMaker?

Accepted Answer

AWS Key Management Service (KMS). AWS Key Management Service (KMS) is the correct service because it provides managed encryption keys that can be used to enable server-side encryption (SSE-KMS) for Amazon S3 buckets. When SageMaker accesses training data from S3, it can use a customer-managed KMS key to encrypt data at rest, ensuring sensitive customer data remains protected. KMS integrates directly with S3 and SageMaker, allowing you to specify a KMS key in the SageMaker training job configuration.

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AWS CloudHSM

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AWS Secrets Manager

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AWS Certificate Manager (ACM)

Question 3

A healthcare company needs to use Amazon SageMaker Ground Truth for data labeling. The data includes protected health information (PHI) that must remain in the US. Which configuration meets the compliance requirements?

Accepted Answer

Use a private workforce consisting of the company's employees and launch the labeling job in the us-east-1 region. Option B is correct because a private workforce consisting of the company's own employees ensures that PHI is never exposed to external workers, and launching the labeling job in us-east-1 keeps all data within the US, satisfying the data residency requirement. Amazon SageMaker Ground Truth allows you to restrict data access to a private workforce that you manage, and by selecting a US region, you ensure that data processing and storage remain within US borders.

Answer

Use a vendor-managed workforce and set up data encryption

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Use a public workforce (Mechanical Turk) and select the US East region

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Use a private workforce but launch the labeling job in the eu-west-1 region

Question 4

A financial institution uses Amazon SageMaker to host a model for credit scoring. The model was trained on data that includes demographic attributes. During a routine audit, the compliance team finds that the model produces significantly different approval rates for applicants of different ethnicities, even when credit profiles are similar. The institution must continue using the model but needs to ensure compliance with fair lending laws. What should the company do FIRST?

Accepted Answer

Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report.. Amazon SageMaker Clarify is the correct first step because it provides built-in bias detection and explainability for machine learning models. Before taking any corrective action, the company must first quantify and understand the nature and extent of the bias using SageMaker Clarify's bias metrics (e.g., Difference in Positive Proportions, Disparate Impact). This diagnostic report is essential for compliance documentation and for determining whether the bias is due to the model, the data, or the threshold, thereby guiding any subsequent remediation steps.

Answer

Adjust the decision threshold to equalize approval rates across groups.

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Document the disparity in a compliance report and continue using the model.

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Replace the model with a simpler explainable model to eliminate bias.

Question 5

A developer is using Amazon Bedrock to generate text summaries. The output sometimes includes irrelevant information. What is the most effective prompt engineering technique to improve relevance?

Accepted Answer

Use few-shot examples with summaries. Few-shot examples provide the model with explicit patterns of desired output, directly guiding it to produce summaries that match the format and content of the examples. This technique is the most effective for improving relevance because it gives the model concrete reference points, reducing the likelihood of including irrelevant information.

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Add a negative prompt specifying what to avoid

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Increase max tokens

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Decrease temperature

Question 6

An e-commerce company uses Amazon Bedrock to generate product descriptions. They notice the descriptions are too long and contain repetitive phrases. Which parameter adjustment can help?

Accepted Answer

Increase frequency penalty. Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or tokens, directly addressing the issue of repetitive language in generated product descriptions. This parameter penalizes tokens that have already appeared in the text, encouraging more diverse output and naturally shortening overly long descriptions by avoiding redundant loops.

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Increase temperature

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Increase top_p

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Decrease presence penalty

Question 7

An organization uses SageMaker JumpStart to deploy a foundation model for real-time inference. They observe high latency. What is the most effective way to reduce latency?

Accepted Answer

Compile the model with SageMaker Neo. SageMaker Neo compiles the model to optimize it for the target hardware, reducing inference latency by applying hardware-specific optimizations such as kernel fusion, quantization, and memory layout tuning. This directly addresses the high latency issue for real-time inference without changing the instance type or inference mode.

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Use a larger instance with more memory

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Use batch transform instead

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Enable SageMaker Inference Recommender

Question 8

An e-commerce company uses Amazon Bedrock to generate product descriptions from keywords. Some descriptions contain inaccurate details about product specifications. Which approach should the company take to reduce factual errors?

Accepted Answer

Include the product specifications in the prompt and instruct the model to base the description on the provided data.. Option D is correct because providing the product specifications directly in the prompt and instructing the model to base the description on that data grounds the generation in factual information, reducing hallucinations. This technique, known as prompt engineering with in-context learning, ensures the model uses the given data rather than relying on its training data, which may contain inaccuracies.

Answer

Increase the maxTokens parameter to allow more detailed descriptions.

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Use a different foundation model from Bedrock for each product category.

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Deploy the model to a SageMaker endpoint and use human-in-the-loop validation.

Question 9

A company runs a chatbot using a large language model on Amazon Bedrock. They notice high latency during peak hours. Which action would be MOST effective to reduce latency without degrading response quality?

Accepted Answer

Use Provisioned Throughput for model inference. Provisioned Throughput on Amazon Bedrock reserves dedicated capacity for model inference, ensuring consistent low latency even during peak hours. This eliminates the variability caused by resource contention in the on-demand tier, directly addressing high latency without altering model size or output quality.

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Increase the number of concurrent invocations

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Switch to a smaller model

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Decrease the maxTokens parameter

Question 10

A company uses Amazon Bedrock to generate code. They want to ensure the code follows security best practices and does not contain vulnerabilities. Which approach is most effective?

Accepted Answer

Use Amazon CodeGuru Security to review generated code.. Amazon CodeGuru Security reviews code for security vulnerabilities and provides recommendations. Using a model trained on secure code may not be sufficient; WAF is for web traffic; training a custom model requires significant effort and may not catch all issues.

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Implement a post-processing step using AWS WAF.

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Train a custom model on the company’s secure code.

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Use a foundation model trained only on secure code.