AIF-C01 Practice Test 14 — 15 Questions

Question 1

Refer to the exhibit. A company sets up a knowledge base for a customer support chatbot using Amazon Bedrock. Users report that the chatbot misses relevant details from long documents. Which change to the data source configuration would most likely improve retrieval?

Accepted Answer

Change chunking strategy to SEMANTIC. The chunking strategy is set to FIXED_SIZE, which may split documents into chunks that are too small or lose context. Switching to SEMANTIC chunking improves retrieval by grouping paragraphs with similar meaning.

Answer

Increase the chunk size in FIXED_SIZE chunking

Answer

Add more documents to the S3 bucket

Answer

Change the embedding model to a larger one

Question 2

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 3

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 4

Which THREE steps are typically involved in fine-tuning a foundation model? (Select THREE.)

Accepted Answer

Prepare a labeled dataset specific to the target domain. Fine-tuning involves preparing a labeled dataset, selecting a pre-trained base model, and training it further on the domain data. Deploying without tuning is not fine-tuning, and selecting a model with more parameters is not a step.

Answer

Deploy the model immediately without additional training

Answer

Choose a model architecture with more parameters than the base model

Question 5

A data scientist is unable to invoke the Claude v2 model from an EC2 instance with IP 10.0.1.5. What is the most likely reason?

Accepted Answer

The resource ARN is incorrect because it should not include the account ID. Option B is correct because the resource ARN is malformed. Amazon Bedrock model ARNs do not include the account ID; the correct format is arn:aws:bedrock:region::foundation-model/anthropic.claude-v2. Option A is not an action. Option C is incorrect because the IP is within the allowed range. Option D is incorrect because the model is available in us-east-1.

Answer

The condition is too restrictive

Answer

The policy does not allow the ec2:AssociateAddress action

Answer

The model is not available in us-east-1

Question 6

A developer is building a chatbot using Amazon Bedrock and Claude. They notice that the model sometimes generates harmful or biased responses. Which AWS service can they use to implement guardrails?

Accepted Answer

Amazon Bedrock Guardrails. Option C, Amazon Bedrock Guardrails, is the native service for adding content filters and safety controls to models in Bedrock. Option A (AWS WAF) is a web application firewall, not for model output. Option B (Amazon GuardDuty) is a threat detection service. Option D (AWS Shield) protects against DDoS attacks.

Answer

AWS WAF

Answer

Amazon GuardDuty

Answer

AWS Shield

Question 7

Which THREE factors should be considered when selecting a foundation model for a text generation task? (Select three.)

Accepted Answer

Context window length. Options A (Context window length), B (Number of parameters), and D (Model license) are key selection criteria. Context window affects input length, parameters affect capability, license affects usage rights. Option C (Inference latency) is operational but often considered after selection. Option E (AWS Region availability) is relevant for deployment but not model selection.

Answer

Inference latency

Answer

AWS Region availability

Question 8

A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?

Accepted Answer

Select a smaller foundation model that still meets accuracy requirements.. Option D is correct because selecting a smaller, efficient foundation model can reduce cost per token while maintaining quality for simple tasks. Option A is wrong because increasing temperature does not reduce cost. Option B is wrong because caching may not be effective for variable outputs. Option C is wrong because fine-tuning increases cost.

Answer

Fine-tune a larger model to improve accuracy and reduce retries.

Answer

Increase the temperature parameter to get shorter responses.

Answer

Cache previous responses to reuse for similar prompts.

Question 9

A company uses Amazon Rekognition for facial analysis. They want to ensure the model doesn't exhibit bias based on skin tone. What should they do?

Accepted Answer

Ensure the training dataset includes diverse skin tones. Option D is correct: Training on diverse data reduces bias. Option A is wrong: Network size does not address bias. Option B is wrong: Data augmentation does not guarantee diversity. Option C is wrong: Pre-trained models may have inherent bias.

Answer

Apply data augmentation to increase dataset size

Answer

Use a larger neural network

Answer

Use a pre-trained model from AWS Marketplace

Question 10

A company is building a chatbot that must provide accurate answers based on internal documents without retraining the model. Which approach should they use?

Accepted Answer

Prompt engineering with retrieval-augmented generation (RAG). RAG (Retrieval-Augmented Generation) retrieves relevant documents and augments the prompt, avoiding retraining. Option A (Fine-tuning) requires retraining. Option C (Distillation) reduces model size. Option D (RLHF) is for aligning model behavior.

Answer

Reinforcement learning from human feedback (RLHF)

Answer

Fine-tuning the model on internal documents

Answer

Model distillation to a smaller model

Question 11

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 12

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 13

A healthcare startup is using Amazon Bedrock to generate clinical notes. They must prevent the model from outputting any personally identifiable information (PII) such as patient names. What is the most effective approach?

Accepted Answer

Configure a guardrail in Amazon Bedrock to deny PII topics. Option B is correct because Amazon Bedrock guardrails provide content filtering that can deny PII topics and block sensitive information. Option A (prompt engineering) can be bypassed. Option C (fine-tuning on de-identified data) is costly and not guaranteed. Option D (regex post-processing) is brittle and incomplete.

Answer

Fine-tune the model on de-identified data only

Answer

Use a prompt engineering technique to instruct the model to avoid PII

Answer

Post-process the output with a regex filter

Question 14

A company is deploying a customer-facing chatbot using Amazon Bedrock. They want to reduce the risk of generating biased or harmful responses. Which TWO measures should they implement? (Choose 2.)

Accepted Answer

Implement a human-in-the-loop review for sensitive replies. Options A and C are correct. Guardrails filter biased/harmful content, and human-in-the-loop review catches nuanced issues. Option B (training on historical conversations) may reinforce existing biases. Option D (high temperature) increases randomness and potential harm. Option E (disabling logging) reduces ability to audit and improve.

Answer

Train the model exclusively on historical customer conversations

Answer

Set the temperature parameter to 1.5

Answer

Disable logging to improve performance

Question 15

A developer is building an application that generates product descriptions from images using a multimodal model. Which AWS service provides access to multimodal foundation models?

Accepted Answer

Amazon Bedrock. Option B, Amazon Bedrock, offers access to multimodal models like Claude 3 that can process images and text. Option A (Rekognition) is for image and video analysis, not generation. Option C (Textract) extracts text from documents. Option D (Comprehend) is for NLP.

Answer

Amazon Rekognition

Answer

Amazon Textract

Answer

Amazon Comprehend

AIF-C01 Practice Test 14 — 15 Questions

Exhibit

AIF-C01 Practice Test 14 — 15 Questions

Exhibit