AIF-C01 Applications of Foundation Models Practice Test 6 — 15 Questions

Question 1

A company uses Amazon Bedrock to build a conversational AI. They want to enforce role-based access to the model. Which AWS service should they use?

Accepted Answer

AWS Identity and Access Management (IAM). AWS Identity and Access Management (IAM) is the correct service because it enables fine-grained, role-based access control (RBAC) to Amazon Bedrock models. You can define IAM policies that specify which principals (users, groups, or roles) are allowed to invoke specific foundation models, ensuring that only authorized roles can interact with the conversational AI.

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AWS Config

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AWS CloudTrail

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AWS Organizations

Question 2

A data scientist uses Amazon Bedrock. The model responses are too long. Which parameter should they adjust to limit the output length?

Accepted Answer

max_tokens. The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) the model can generate in a single response. By reducing this value, the data scientist caps the output length, preventing overly long responses. Temperature and top_p affect randomness and diversity, not length, while stop sequences define when generation halts but do not enforce a hard token limit.

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temperature

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stop sequences

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top_p

Question 3

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.

Question 4

A company uses Amazon Bedrock to generate marketing copy. They want to measure the quality of generated text compared to reference text. Which metric is most appropriate?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is the most appropriate metric for evaluating the quality of generated text against reference text in tasks like machine translation and text generation. It measures n-gram precision between the generated and reference texts, making it ideal for assessing marketing copy generated by Amazon Bedrock.

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F1 score

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RMSE

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Accuracy

Question 5

A company uses Amazon Bedrock with a custom model deployed via Amazon SageMaker. They want to monitor for data drift in input prompts over time. Which AWS service is best suited for this?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct choice because it is specifically designed to detect data drift in machine learning models, including input prompts for custom models deployed via SageMaker. It continuously monitors the distribution of input data against a baseline and alerts when drift occurs, which aligns with the requirement to monitor input prompts over time.

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Amazon CloudWatch

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AWS CloudTrail

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Amazon Athena

Question 6

A company is using Amazon Bedrock to generate images. They want to ensure the outputs comply with content policies. Which TWO AWS services can help? (Choose two.)

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) is correct because it enables human review of model predictions to ensure compliance with content policies. For image generation on Bedrock, A2I can route outputs that fall below a confidence threshold to human reviewers, providing a safety net for policy adherence.

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Amazon GuardDuty

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AWS WAF

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Amazon Comprehend

Question 7

A data science team is fine-tuning a foundation model on Amazon SageMaker. Which THREE steps are part of the best practice? (Choose three.)

Accepted Answer

Monitor for catastrophic forgetting during fine-tuning.. Catastrophic forgetting is a known risk when fine-tuning foundation models, where the model loses previously learned knowledge while adapting to new data. Monitoring for this during fine-tuning on SageMaker allows the team to detect performance degradation on the original task and adjust training accordingly, ensuring the model retains its general capabilities.

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Increase model size to improve performance.

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Deploy the model to production immediately after fine-tuning.

Question 8

A company uses Amazon Bedrock to build a question-answering system. Which THREE features of Amazon Bedrock can improve answer accuracy? (Choose three.)

Accepted Answer

Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) improves answer accuracy by retrieving relevant, up-to-date information from external knowledge bases (e.g., Amazon OpenSearch Serverless or Aurora) and providing it as context to the foundation model. This grounds the model's response in factual data, reducing hallucinations and enabling accurate answers without retraining.

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Auto-scaling of provisioned throughput

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Encryption at rest

Question 9

A security engineer creates the above IAM policy to allow a user to invoke an Amazon Bedrock model. However, invocation fails. What is the issue?

Accepted Answer

The ARN should use "foundation-model" instead of "model".. The IAM policy's resource ARN incorrectly uses 'model' in the path, but Amazon Bedrock requires 'foundation-model' to reference foundation models. The correct ARN format for invoking a Bedrock foundation model is 'arn:aws:bedrock:region::foundation-model/model-id'. Using 'model' instead of 'foundation-model' causes the policy to not match any valid Bedrock resource, resulting in an invocation failure.

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The action should be "bedrock:InvokeModelWithResponseStream".

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The resource ARN is missing the account ID.

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The statement is missing a condition for the model ID.

Question 10

A developer invokes an Amazon Bedrock model and receives the above response. What does the 'stopReason' field indicate?

Accepted Answer

The model reached a defined stop sequence.. The 'stopReason' field in an Amazon Bedrock response indicates why the model stopped generating tokens. When set to 'stop', it means the model encountered a defined stop sequence (such as a special token like <|endoftext|> or a user-specified string) and halted generation normally. This is the expected behavior for a successful, complete response.

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The model encountered an error.

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The model hit the maximum token limit.

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The model stopped due to a safety filter.

Question 11

A company uses Amazon Bedrock to build a conversational AI. They want to enforce role-based access to the model. Which AWS service should they use?

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

Accepted Answer

Amazon Kendra. Amazon Kendra is a fully managed intelligent search service that can be directly integrated with Amazon Bedrock to implement Retrieval Augmented Generation (RAG) without any infrastructure management. It indexes internal company documents and retrieves relevant passages, which are then passed to the foundation model as context to generate accurate, grounded answers.

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Amazon OpenSearch Service

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Amazon DynamoDB

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Amazon RDS

Question 12

A company fine-tunes a foundation model on SageMaker JumpStart for sentiment analysis. After deployment, the model shows bias toward positive sentiment. Which action should be taken to mitigate bias?

Accepted Answer

Perform RLHF (Reinforcement Learning from Human Feedback) to align outputs. RLHF (Reinforcement Learning from Human Feedback) is the correct approach because it directly addresses the misalignment between the model's outputs and desired human values. By collecting human feedback on model outputs and using it to train a reward model, RLHF fine-tunes the foundation model to reduce biased behavior, such as the over-prediction of positive sentiment, without simply reweighting the training data.

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Use a different foundation model

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Add more positive examples to training data

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Increase training epochs

Question 13

A startup needs to generate product descriptions from bullet points using a foundation model. They want a fully managed serverless experience. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed serverless service that provides access to foundation models (FMs) from leading AI providers via an API, making it ideal for generating product descriptions from bullet points. It eliminates infrastructure management while allowing you to invoke models like Anthropic Claude or Amazon Titan for text generation tasks.

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Amazon Comprehend

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Amazon Polly

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Amazon Lex

Question 14

A company fine-tunes a foundation model on SageMaker using a custom dataset. They notice the training job takes too long. Which optimization technique is specifically designed to reduce training time for foundation models?

Accepted Answer

Distributed training using SageMaker Data Parallelism. SageMaker Data Parallelism distributes the training workload across multiple GPUs or instances, splitting the data and synchronizing gradients using optimized all-reduce algorithms. This specifically reduces training time for large foundation models by enabling parallel computation, which is the most direct technique for accelerating training at scale.

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Using a smaller instance type

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Using Spot Instances

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Reducing batch size

Question 15

An enterprise deploys a foundation model on Amazon Bedrock with a knowledge base. Users report that the model is returning outdated information. What is the most likely cause?

Accepted Answer

The knowledge base data source is not refreshed. When a knowledge base is attached to a foundation model on Amazon Bedrock, the model retrieves information from the data source to augment its responses. If the data source is not refreshed, the model will return outdated information even if the model itself is current. Option C directly addresses this by identifying the stale data source as the root cause.

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The model was fine-tuned

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The model is not the latest version

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The inference parameters are incorrect