AIF-C01 Practice Test 16 — 15 Questions

Question 1

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 2

A company wants to generate product descriptions from a few keywords without managing infrastructure. Which AWS service provides a serverless API for accessing foundation models?

Accepted Answer

Amazon Bedrock. Amazon Bedrock offers a serverless API to invoke foundation models. Option A (SageMaker) is for training and deployment, not serverless API. Option C (Comprehend) is for NLP analysis, not generative. Option D (Lex) is for conversational interfaces.

Answer

Amazon Lex

Answer

Amazon SageMaker

Answer

Amazon Comprehend

Question 3

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 4

Refer to the exhibit. A developer receives an error when trying to invoke the Claude Instant model from an application. The application uses the IAM role 'MyAppRole'. Which IAM policy statement should be added to the role to resolve the error?

Accepted Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1"}. The error indicates missing permission to invoke the specific model. The correct action is 'bedrock:InvokeModel' on the specific model ARN.

Answer

{"Effect":"Allow","Action":"bedrock:GetFoundationModel","Resource":"*"}

Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/*"}

Answer

{"Effect":"Allow","Action":"bedrock:*","Resource":"*"}

Question 5

An application uses this configuration to enable RAG. What is required for the knowledge base to function?

Accepted Answer

The knowledge base must have a vector index configured in Amazon OpenSearch Serverless. Option A is correct because a vector index (e.g., in Amazon OpenSearch Serverless) is necessary to store and retrieve embeddings. Option B (fine-tune embedding model) is optional. Option C (internet access) is not needed. Option D (account ID in ARN) is incorrect format.

Answer

The agent must have internet access to retrieve documents

Answer

The embedding model ARN must include the account ID

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The embedding model must be fine-tuned on the domain data

Question 6

A company is using Amazon Bedrock to summarize long documents. They notice that the summary sometimes omits key details. What is the most likely cause?

Accepted Answer

The model's context window is too small. Option A, the model's context window is too small, causes the model to only see part of the document, resulting in omitted details. Option B (temperature too high) increases randomness, not omission. Option C (lack of examples) may affect quality but not omission due to length. Option D (overfitting) would affect performance on new data, not specifically omission of details.

Answer

The model is overfitted

Answer

The prompt lacks examples

Answer

The temperature parameter is too high

Question 7

Refer to the exhibit. A developer wants to choose a model that can generate text (not just embeddings) and has the lowest cost. Based on the exhibit, which model should they select?

Accepted Answer

Titan Text Lite. Option A, Titan Text Lite, is a text generation model and is the lighter, cheaper option compared to Express. Titan Text Express is more expensive. Titan Embed Text is for embeddings, not text generation. Therefore, Titan Text Lite is correct.

Answer

Titan Embed Text

Answer

Titan Text Express

Answer

Need more information

Question 8

Which THREE are best practices for building a secure and scalable generative AI application using Amazon Bedrock? (Choose 3)

Accepted Answer

Implement guardrails to filter harmful content. Using multiple foundation models through Bedrock's multi-model support allows flexibility and best-of-breed selection. Guardrails provide content safety. KMS encryption protects data at rest and in transit. Storing keys in source code is insecure. EC2 deployment is not applicable to Bedrock's serverless model.

Answer

Deploy models on EC2 instances for better control

Answer

Store API keys in source code for easy access

Question 9

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 10

What is a foundation model?

Accepted Answer

A model trained on diverse data that can be adapted to many tasks. A foundation model is a large-scale neural network trained on vast amounts of data, which can be adapted to various downstream tasks through fine-tuning or prompt engineering.

Answer

A model that only works with tabular data

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A model that requires no additional tuning for new tasks

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A model that is specifically trained for one task, like image classification

Question 11

A developer is testing different prompts for a text generation model on Amazon Bedrock. Which parameter controls the randomness of the model's output?

Accepted Answer

temperature. Option D is correct because temperature controls the randomness of the model's predictions. Lower values make output more deterministic; higher values increase randomness. Option A (max_tokens) controls output length. Option B (top_p) is nucleus sampling. Option C (stop_sequences) defines stopping criteria.

Answer

top_p

Answer

stop_sequences

Answer

max_tokens

Question 12

A developer invoked an Amazon Bedrock model and received the following error: 'ValidationException: 1 validation error detected: Value 'claude-instant-v1' at 'modelId' failed to satisfy constraint: Member must satisfy enum value set: [ai21.j2-mid-v1, amazon.titan-text-lite-v1, anthropic.claude-v2, ...]'. What is the likely cause?

Accepted Answer

The modelId is not part of the allowed enum of models for the account. Option C is correct because the error indicates the modelId 'claude-instant-v1' is not in the allowed enum set. This is usually because the model ID is incorrectly spelled or not available in this region/account. Option A (deprecated) would give a different message. Option B (region availability) would mention region. Option D (permissions) would be a different error type.

Answer

The Lambda function does not have the necessary IAM permissions

Answer

The modelId is not available in the current AWS region

Answer

The modelId is deprecated and has been renamed

Question 13

A company wants to automate the extraction of key information from customer support tickets using generative AI. They have a small labeled dataset. Which approach would be most effective?

Accepted Answer

Fine-tune a foundation model on the labeled data. Option C, fine-tuning a foundation model on the labeled data, is most effective with a small dataset as it adapts the model to the specific task without needing massive data. Option A (training from scratch) requires large datasets. Option B (zero-shot) may not be accurate enough. Option D (Comprehend custom entities) is a traditional approach that may also work but fine-tuning often yields better results with generative AI.

Answer

Use zero-shot prompting with a foundation model

Answer

Train a custom model from scratch

Answer

Use Amazon Comprehend custom entity recognition

Question 14

A financial services company is subject to strict regulatory requirements. They plan to use generative AI to summarize customer interaction logs. Which combination of AWS services and configurations best ensures compliance while maintaining accuracy?

Accepted Answer

Use Amazon Bedrock with a private VPC endpoint, AWS KMS encryption, and content filtering.. Option D is correct because Amazon Bedrock with a private VPC endpoint and data encryption at rest and in transit ensures data sovereignty, and using a foundation model that supports content filtering reduces risk of non-compliant outputs. Option A is wrong because Bedrock does not support local on-premises deployment. Option B is wrong because SageMaker alone does not provide built-in content filtering. Option C is wrong because using a public API key violates security policies.

Answer

Deploy an open-source model on Amazon Bedrock in a local on-premises server.

Answer

Use Amazon Bedrock with a foundation model and public internet access without encryption.

Answer

Use Amazon SageMaker to host a fine-tuned model with a public API key.

Question 15

Which TWO techniques provide interpretability for machine learning models at a local (per-prediction) level? (Choose two.)

Accepted Answer

SHAP values. Options A and C are correct. LIME and SHAP are local interpretability methods. Option B (Permutation importance) is global. Option D (Partial dependence) is global. Option E (Confusion matrix) is a performance metric, not interpretability.

Answer

Partial dependence plots

Answer

Confusion matrix

Answer

Permutation feature importance