AIF-C01 Practice Test 18 — 15 Questions

Question 1

A developer invoked an Amazon Bedrock model and received this output. What does the stopReason field indicate?

Accepted Answer

The model reached the maximum token limit set in the request. Option A is correct because stopReason 'max_tokens' means the model stopped because it reached the maximum token limit specified in the request. Option B (natural stop) would be 'stop' or 'end_turn'. Option C (input too long) would be a different error. Option D (content filter) would be 'content_filtered'.

Answer

A content filter blocked the output

Answer

The input prompt was too long

Answer

The model reached a natural stopping point

Question 2

A company is using Amazon Bedrock to generate marketing copy. They want to ensure the model's responses are factually accurate and grounded in their proprietary knowledge base. Which feature should they use?

Accepted Answer

Retrieval Augmented Generation (RAG). Option B, Retrieval Augmented Generation (RAG), retrieves relevant information from the company's knowledge base to ground the model's responses, improving factual accuracy. Option A (Model customization) tailors the model's behavior but does not necessarily ground responses in real-time data. Option C (Prompt engineering) relies on crafting prompts, which may not guarantee factual accuracy. Option D (Fine-tuning) updates model weights but may not incorporate up-to-date knowledge.

Answer

Model customization

Answer

Fine-tuning

Answer

Prompt engineering

Question 3

A company wants to evaluate the performance of a generative AI model before deployment. Which TWO metrics are most relevant for measuring model quality? (Select two.)

Accepted Answer

BLEU score. Options A (BLEU score) and C (Perplexity) are standard for evaluating text generation quality. BLEU measures similarity to reference text, and perplexity measures how well the model predicts a sample. Option B (CPU utilization) is operational, not quality. Option D (Response time) is latency. Option E (Model size) is a design parameter.

Answer

Response time

Answer

Model size

Answer

CPU utilization

Question 4

A startup wants to quickly prototype a generative AI application for summarizing news articles. They have limited ML expertise and want minimal infrastructure management. Which AWS service should they use?

Accepted Answer

Amazon Bedrock with a foundation model accessed via API.. Option C is correct because Amazon Bedrock provides serverless access to foundation models via API, requiring no ML infrastructure. Option A is wrong because Amazon SageMaker requires managing training jobs and endpoints. Option B is wrong because AWS Lambda is a compute service, not a generative AI service. Option D is wrong because Amazon EC2 requires manual setup of models.

Answer

Amazon SageMaker to build and train a custom summarization model.

Answer

AWS Lambda with a custom Python script using the Hugging Face Transformers library.

Answer

Amazon EC2 instance running a pre-trained model from AWS Marketplace.

Question 5

A company is building a multi-step AI agent using Amazon Bedrock Agents to automate a complex business process that requires memory across interactions. The agent needs to remember user preferences and previous steps. Which approach best maintains state across sessions?

Accepted Answer

Leverage Amazon Bedrock Agents' built-in memory feature. Amazon Bedrock Agents have built-in memory management (session memory) that automatically persists context across interactions, using a backend like DynamoDB. Lambda would require custom state management. Step Functions orchestrate but don't inherently store memory. DynamoDB alone lacks the agent logic.

Answer

Store state in AWS Lambda environment variables

Answer

Use Amazon DynamoDB tables managed by Lambda functions

Answer

Implement state management using AWS Step Functions

Question 6

A machine learning team is fine-tuning a foundation model using Amazon SageMaker. They need to optimize training time and cost. Which approach should they take?

Accepted Answer

Use Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA. Option B is correct because Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA only update a small subset of parameters, significantly reducing compute requirements. Option A (full model weights on single GPU) is slow and expensive. Option C (maximum batch size) may cause out-of-memory errors. Option D (larger instance) increases cost without necessarily improving efficiency.

Answer

Use a larger instance type with more vCPUs

Answer

Increase the batch size to the maximum possible

Answer

Use the full model weights and train on a single GPU

Question 7

A data scientist wants to fine-tune a foundation model on a specific domain dataset using Amazon SageMaker. Which built-in SageMaker feature can simplify the training process?

Accepted Answer

SageMaker JumpStart. Option A, SageMaker JumpStart, provides pre-trained foundation models and built-in fine-tuning scripts, simplifying the process. Option B (Ground Truth) is for data labeling. Option C (Neo) is for model optimization. Option D (Canvas) is a no-code ML tool for business analysts.

Answer

SageMaker Neo

Answer

SageMaker Canvas

Answer

SageMaker Ground Truth

Question 8

Refer to the exhibit. A developer is optimizing latency for a generative AI model deployed on SageMaker. Based on the exhibit, which change would most likely reduce per-token latency?

Accepted Answer

Reduce model size through quantization. Option C, reduce model size through quantization, directly reduces computation per token, lowering latency. Option A (larger instance) may help but is less targeted. Option B (increase batch size) improves throughput but not per-token latency. Option D (CPU instance) would increase latency.

Answer

Use a CPU instance

Answer

Switch to a larger instance type

Answer

Increase batch size to 10

Question 9

A company operates in a region where Amazon Bedrock is not available. They want to use generative AI but must keep data within the country. Which solution should they consider?

Accepted Answer

Use Amazon SageMaker to host an open-source model in the local region.. Option A is correct because Amazon SageMaker can deploy models in any AWS region, including those without Bedrock, and can use custom models with data staying in-region. Option B is wrong because cross-region inference sends data outside the country. Option C is wrong because using a third-party model outside AWS may not comply. Option D is wrong because there is no region-constrained Bedrock offering.

Answer

Wait for Bedrock to become available in their region; there is no alternative.

Answer

Use Amazon Bedrock in the nearest available region with cross-region inference.

Answer

Use an API from a third-party generative AI provider with AWS PrivateLink.

Question 10

A data scientist fine-tuned a large language model on Amazon SageMaker for financial report generation. The model produces responses that are too short and incomplete, often cutting off mid-sentence. What parameter should be adjusted first?

Accepted Answer

Increase the maximum token count. The max tokens parameter limits the length of generated responses. Increasing it allows the model to produce longer completions. Temperature, top_p, and model change affect quality or diversity, but not the length cap.

Answer

Increase the temperature parameter

Answer

Increase the top_p parameter

Answer

Switch to a different foundation model

Question 11

A company is building a generative AI application to generate product descriptions from customer reviews. They want to use Amazon Bedrock to access a foundation model. Which TWO actions should the company take to ensure responsible AI practices?

Accepted Answer

Implement human review of all generated descriptions before publication.. Options A and C are correct. Implementing human review (A) ensures oversight and catches harmful outputs. Monitoring and logging (C) enables auditing and detection of misuse. Option B is incorrect because using a single model does not automatically avoid bias; customization may be needed. Option D is incorrect because disabling content filtering increases risk of generating inappropriate content. Option E is plausible but not a requirement specific to responsible AI; evaluation is part of ongoing improvement but not the immediate action.

Answer

Use a single foundation model without any customization to avoid bias.

Answer

Regularly evaluate model performance and fine-tune with diverse data.

Answer

Disable content filtering to allow maximum creativity.

Question 12

An enterprise wants to ensure that generative AI applications built on AWS comply with data privacy regulations. They need to prevent the model from using customer data in future training. Which feature of Amazon Bedrock should they enable?

Accepted Answer

Opt-out of model improvement. Option D, opt-out of model improvement, prevents AWS from using customer data for service improvement and training. Option A (encryption) protects data at rest but does not prevent use in training. Option B (model customization) may actually use customer data. Option C (policy-based data governance) is not a specific Bedrock feature for this purpose.

Answer

Policy-based data governance

Answer

Data encryption at rest

Answer

Model customization with customer data

Question 13

A company is building a customer support chatbot using Amazon Bedrock. They have a large corpus of internal documentation and want to provide accurate answers without retraining the model. Which approach should they use?

Accepted Answer

Use Retrieval Augmented Generation (RAG) with Amazon Bedrock Knowledge Base.. Option C is correct because Retrieval Augmented Generation (RAG) with Bedrock Knowledge Base allows the model to retrieve relevant documents from internal sources and generate grounded answers, avoiding the need for fine-tuning. Option A is wrong because a pre-trained model alone lacks domain knowledge. Option B is wrong because fine-tuning requires labeled data and is more costly. Option D is wrong because prompt engineering alone cannot incorporate proprietary data effectively.

Answer

Use advanced prompt engineering with a generic foundation model.

Answer

Use a pre-trained foundation model without customization.

Answer

Fine-tune the model on their documentation using Amazon SageMaker.

Question 14

Which TWO factors are most important when selecting a foundation model in Amazon Bedrock for a text summarization task with strict latency requirements?

Accepted Answer

Average response latency per request.. Options A and B are correct. Response latency directly impacts user experience, so a model with low latency is essential. Output quality/token ensures the summaries are accurate and concise. Option C is wrong because fine-tuning increases cost and latency. Option D is wrong because model size affects latency but latency itself is the direct factor. Option E is wrong because input token limit is relevant but not as critical as latency and quality for this use case.

Answer

Model size in billions of parameters.

Answer

Maximum input token limit.

Answer

Availability of fine-tuning capability for domain adaptation.

Question 15

Which TWO actions would improve the grounding of responses from a generative AI model using RAG? (Choose 2)

Accepted Answer

Use RAG with a knowledge base of relevant documents. Using RAG with relevant documents directly grounds responses in factual data. Including source citations in prompts encourages the model to base answers on retrieved information. Increasing temperature or reducing context would likely hurt grounding. Fine-tuning on unrelated data does not help.

Answer

Fine-tune the model on unrelated data

Answer

Reduce the context window to save tokens

Answer

Increase the model's temperature parameter

AIF-C01 Practice Test 18 — 15 Questions

Exhibit

AIF-C01 Practice Test 18 — 15 Questions

Exhibit