AIF-C01 Fundamentals of Generative AI Practice Test 3 — 15 Questions

Question 1

A developer is using Amazon Bedrock's Converse API to build a multi-turn conversation. They notice the model forgets earlier context after a few exchanges. What is the most likely cause?

Accepted Answer

The developer is not sending the previous messages in each request. The API requires passing the message history explicitly; if not, context is lost. Option A (Rate limits) affect throughput. Option B (Model capacity) is not typical. Option D (Output length) truncates responses.

Answer

The API has a rate limit that truncates history

Answer

The model's context window is too small for the conversation

Answer

The model's maximum output length is set too low

Question 2

Which TWO actions are best practices for reducing hallucinations in generative AI models? (Choose 2)

Accepted Answer

Use retrieval-augmented generation (RAG). RAG provides factual grounding, and prompt engineering with clear constraints reduces hallucinations. Increasing model size (A) may not reduce hallucinations. Fine-tuning (C) can help but is not a direct best practice for all cases; RAG and prompt engineering are more effective. Using a smaller model (E) may increase hallucinations.

Answer

Increase the model size

Answer

Fine-tune the model on proprietary data

Answer

Use a smaller model to limit complexity

Question 3

Which THREE are key capabilities of Amazon Bedrock? (Choose 3)

Accepted Answer

Model customization through fine-tuning. Bedrock offers serverless inference (A), model customization (B), and guardrails for content filtering (D). Built-in vector database (C) is not a Bedrock capability; Bedrock integrates with external vector stores. Auto model selection (E) is not available; users choose models explicitly.

Answer

Automatic model selection based on use case

Answer

Built-in vector database for knowledge bases

Question 4

Which TWO factors are most important when selecting a foundation model for a sentiment analysis task? (Choose 2)

Accepted Answer

Composition of the model's training data (domain, language). Model size and training data composition directly impact performance on sentiment. API pricing (C) is a business factor but less critical for model selection; latency (E) is also a factor but not as fundamental as size and data quality.

Answer

API pricing per invocation

Answer

Inference latency

Answer

Color scheme of the model's documentation

Question 5

A developer runs this AWS CLI command to invoke a model in us-west-2 but receives an error: 'An error occurred (ModelNotFoundException) when calling the InvokeModel operation: Model not found'. What is the most likely cause?

Accepted Answer

The model is not available in the us-west-2 region. The model anthropic.claude-v2 is not available in us-west-2; it is available in us-east-1. Option A (permissions) would give AccessDenied. Option B (body format) would give validation error. Option D (region missing) is not the issue as region is specified.

Answer

The request body is not properly formatted

Answer

The --region parameter is missing from the command

Answer

The user's IAM role lacks permissions to invoke the model

Question 6

A user has this IAM policy and attempts to invoke the model in the us-west-2 region. They receive an AccessDenied error. What is the reason?

Accepted Answer

The policy does not allow the us-west-2 region. The resource ARN specifies us-east-1, so the policy grants access only in that region. Option B (action) is correct but not the issue. Option C (case sensitivity) is not relevant. Option D (malformed) is false.

Answer

The resource ARN is malformed

Answer

The action 'bedrock:InvokeModel' is incorrect

Answer

The model ID is case-sensitive

Question 7

A developer deployed this guardrail to block sensitive topics and sexual content. However, the model still generates responses about a specific sensitive topic that is not in the TopicPolicy. What should the developer do to prevent this?

Accepted Answer

Add the specific topic to the TopicPolicy list. The guardrail's TopicPolicy only blocks the defined topic 'sensitive-topic'. To block additional topics, add them to the list. Option A (change type) would allow. Option B (SensitiveInformationPolicy) is for PII. Option C (increase strength) does not add topics.

Answer

Add a SensitiveInformationPolicy to filter PII

Answer

Increase the InputStrength of the content filter to MAX

Answer

Change the TopicPolicy Type from DENY to ALLOW

Question 8

A company is building a customer service chatbot using Amazon Bedrock. Which component of a foundation model determines the creativity and randomness of the generated responses?

Accepted Answer

Temperature. The temperature parameter controls randomness. Higher values (e.g., >1) produce more creative but less focused outputs, while lower values (e.g., near 0) produce more deterministic responses.

Answer

Prompt template

Answer

Max tokens

Answer

Top-p

Question 9

A retail company wants to generate product descriptions from catalog data. The data includes structured attributes (e.g., price, brand) and unstructured reviews. The team needs to ensure factual accuracy. Which approach is most appropriate?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a knowledge base. Retrieval-Augmented Generation (RAG) retrieves relevant documents (product attributes, reviews) and provides them as context to the model, reducing hallucinations and grounding responses in facts.

Answer

Use prompt engineering with few-shot examples

Answer

Fine-tune a foundation model on the entire product catalog

Answer

Deploy a larger foundation model with more parameters

Question 10

A data scientist is deploying a fine-tuned Mistral model on Amazon Bedrock. After deployment, inference latency is too high for real-time applications. Which configuration change can reduce latency without significantly impacting output quality?

Accepted Answer

Reduce max tokens from 1024 to 256. Reducing the max tokens limit decreases the number of generated tokens, directly reducing latency. Lowering temperature or using a larger model may not help or may degrade quality.

Answer

Switch to a larger model variant

Answer

Decrease the top-p to 0.5

Answer

Increase the temperature to 0.9

Question 11

Which AWS service provides a serverless experience for building and scaling generative AI applications with access to various foundation models?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models through a single API, without managing the underlying infrastructure.

Answer

Amazon SageMaker

Answer

Amazon Lex

Answer

AWS Lambda

Question 12

A startup is building an AI-powered code assistant using a large language model (LLM). They want to ensure the model generates syntactically correct code and avoids security vulnerabilities. Which technique should they prioritize?

Accepted Answer

Augment prompts with few-shot examples of secure coding practices and unit tests. Contextual grounding by providing code examples and security guidelines in the prompt (prompt engineering) helps guide the model to produce safe and correct code. Fine-tuning on secure codebases would also help but is more resource-intensive; prompt engineering is a quicker first step.

Answer

Deploy the model with max tokens set to 4096

Answer

Fine-tune the model on a large corpus of open-source code

Answer

Use chain-of-thought prompting to explain reasoning before code generation

Question 13

A bank is using Amazon Bedrock to summarize customer support transcripts. The summaries often contain factual inaccuracies (hallucinations). Which approach is most effective for reducing hallucinations?

Accepted Answer

Implement RAG by grounding summarization on retrieved transcripts. Retrieval-Augmented Generation (RAG) grounds the model's output on retrieved transcripts, reducing the chance of fabricating details. Fine-tuning on transcripts may reinforce patterns but does not guarantee factual accuracy at inference time.

Answer

Decrease the top-p to 0.1

Answer

Increase the model's temperature to make outputs more diverse

Answer

Fine-tune a smaller model on a large dataset of transcripts

Question 14

A media company uses a foundation model on Amazon Bedrock to generate article summaries. The model occasionally omits important details. Which prompt engineering technique is most likely to improve completeness?

Accepted Answer

Include a list of required key points in the prompt. Providing a structured output format (e.g., bullet points, required sections) helps the model cover all aspects, reducing omissions.

Answer

Use a lower temperature setting

Answer

Increase the max tokens limit

Answer

Add 'Be concise' to the prompt

Question 15

A company fine-tunes a foundation model using SageMaker to create a domain-specific chatbot. After deployment on Bedrock, the model shows high confidence in incorrect answers. What is the most likely cause and its solution?

Accepted Answer

The model is overfitting; apply regularization techniques during fine-tuning. Overfitting during fine-tuning can cause the model to be overly confident even when wrong. Regularization (e.g., early stopping, dropout) reduces overconfidence.

Answer

The model was not pre-trained on enough data; use a larger base model

Answer

The training data was imbalanced; collect more diverse data

Answer

The inference temperature is too low; increase it