AIF-C01 Practice Test 2 — 10 Questions

Question 1

A company is using Amazon Bedrock to generate content for a marketing application. The company wants to ensure that the model does not generate content that violates the company's brand guidelines, which prohibit certain keywords and tones. Which TWO features should the company use to enforce these guidelines? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create a prompt template that instructs the model to adhere to brand guidelines and avoid prohibited keywords.. Prompt engineering allows the company to embed brand guidelines directly into the instruction given to the model, effectively steering the output away from prohibited keywords and tones. Option C is correct because Amazon Bedrock Guardrails provides a managed, policy-based mechanism to define custom deny topics and content filters that can block or mask unwanted content at inference time, enforcing brand guidelines without manual intervention.

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Enable Amazon CloudWatch Logs to capture model output and manually review.

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Use AWS IAM policies to restrict the model's output to only approved words.

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Encrypt the model responses using AWS KMS to prevent unauthorized viewing.

Question 2

Which AWS service provides access to a wide variety of foundation models from different providers through a single API, without managing underlying infrastructure?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to a wide variety of foundation models (FMs) from leading AI providers such as Anthropic, Meta, Stability AI, and Amazon itself through a single API. It eliminates the need to manage underlying infrastructure, such as GPU clusters or model hosting, allowing developers to integrate generative AI capabilities directly into applications.

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Amazon SageMaker

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AWS Lambda

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Amazon Rekognition

Question 3

A healthcare organization uses an ML model to predict patient readmission risk. The model performs well overall but has significantly higher false negative rates for elderly patients. The team needs to mitigate this bias. Which step should they take FIRST?

Accepted Answer

Use SageMaker Clarify to compute bias metrics and identify the extent of the disparity. The first step is to identify and understand the bias through measurement. SageMaker Clarify can compute fairness metrics like false negative rate difference to quantify the disparity. Retraining with fairness constraints or reweighting are mitigation steps that come after measurement.

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Retrain the model with fairness constraints using SageMaker Clarify

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Remove the age feature from the model

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Collect more data for elderly patients to reduce representation bias

Question 4

A startup is using Amazon Bedrock to power a virtual assistant. They need to ensure that personally identifiable information (PII) is not included in the model's responses. Which feature should they enable?

Accepted Answer

Enable PII redaction in the Bedrock guardrails.. Amazon Bedrock Guardrails provide a configurable content filtering and PII redaction feature that can automatically detect and mask personally identifiable information (PII) in model inputs and outputs. By enabling PII redaction within guardrails, the startup can ensure that sensitive data like names, addresses, or credit card numbers are removed or obfuscated before the virtual assistant's responses reach the user. This is the direct and intended mechanism for preventing PII leakage in model responses.

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Enable model invocation logging.

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Configure a VPC endpoint.

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Enable data encryption at rest.

Question 5

A company needs to audit all API calls made to Amazon Bedrock, including model invocations and guardrail evaluations. Which AWS service should they enable to capture these API calls for compliance?

Accepted Answer

AWS CloudTrail. AWS CloudTrail records API calls for all AWS services, including Bedrock. It captures the caller identity, API, parameters, and response elements, which can be used for auditing and compliance.

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Amazon Macie

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AWS Config

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Amazon GuardDuty

Question 6

A team is fine-tuning a foundation model using SageMaker. They want to minimize training time while keeping the model's original knowledge. Which technique is BEST suited?

Accepted Answer

Use Parameter Efficient Fine-Tuning (PEFT) such as LoRA. Parameter Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are best suited because they freeze the pre-trained model weights and inject trainable low-rank matrices into specific layers, drastically reducing the number of trainable parameters. This minimizes training time and computational cost while preserving the model's original knowledge, as only a small fraction of parameters are updated during fine-tuning.

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Use distributed training across multiple GPUs

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Use prompt engineering instead of fine-tuning

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Full fine-tuning on the new dataset

Question 7

Which of the following is an example of reinforcement learning?

Accepted Answer

A robot learning to navigate a maze by receiving rewards for reaching the goal. Reinforcement learning involves an agent learning to make decisions by interacting with an environment and receiving rewards or penalties for its actions. Option D describes a robot learning to navigate a maze by receiving rewards for reaching the goal, which is a classic example of an agent optimizing its policy through trial and error to maximize cumulative reward.

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Predicting house prices based on historical data

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Grouping customers into segments based on purchasing behavior

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Detecting spam emails using labeled examples

Question 8

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 9

A company is building a resume screening model and discovers that the training data contains only resumes from one gender, leading to biased predictions. Which type of bias does this represent, and what is the most effective mitigation strategy?

Accepted Answer

Representation bias; mitigate by collecting more diverse training data. Representation bias occurs when certain groups are underrepresented in the training data. Mitigation includes collecting more diverse data or using techniques like re-weighting or synthetic data generation.

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Aggregation bias; mitigate by using a single model for all groups

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Measurement bias; mitigate by using more precise measurement tools

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Historical bias; mitigate by removing sensitive attributes from the model

Question 10

A company needs to summarize thousands of customer reviews daily using a foundation model. The solution must minimize latency and cost while handling variable traffic. Which AWS service should they use?

Accepted Answer

Amazon Bedrock with on-demand mode. Amazon Bedrock with on-demand mode is correct because it provides serverless access to foundation models (FMs) with pay-per-use pricing, which minimizes cost for variable traffic and eliminates the need to provision infrastructure. The on-demand mode handles thousands of daily summarization requests with low latency by leveraging AWS's scalable inference infrastructure, making it ideal for variable workloads without upfront commitments.

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Amazon SageMaker real-time endpoint

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Amazon Comprehend

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Amazon Lex