AIF-C01 Practice Test 5 — 25 Questions

Question 1

An e-commerce company uses Amazon Bedrock to generate product descriptions from keywords. Some descriptions contain inaccurate details about product specifications. Which approach should the company take to reduce factual errors?

Accepted Answer

Include the product specifications in the prompt and instruct the model to base the description on the provided data.. Option D is correct because providing the product specifications directly in the prompt and instructing the model to base the description on that data grounds the generation in factual information, reducing hallucinations. This technique, known as prompt engineering with in-context learning, ensures the model uses the given data rather than relying on its training data, which may contain inaccuracies.

Answer

Increase the maxTokens parameter to allow more detailed descriptions.

Answer

Use a different foundation model from Bedrock for each product category.

Answer

Deploy the model to a SageMaker endpoint and use human-in-the-loop validation.

Question 2

A company runs a chatbot using a large language model on Amazon Bedrock. They notice high latency during peak hours. Which action would be MOST effective to reduce latency without degrading response quality?

Accepted Answer

Use Provisioned Throughput for model inference. Provisioned Throughput on Amazon Bedrock reserves dedicated capacity for model inference, ensuring consistent low latency even during peak hours. This eliminates the variability caused by resource contention in the on-demand tier, directly addressing high latency without altering model size or output quality.

Answer

Increase the number of concurrent invocations

Answer

Switch to a smaller model

Answer

Decrease the maxTokens parameter

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.

Answer

Implement a post-processing step using AWS WAF.

Answer

Train a custom model on the company’s secure code.

Answer

Use a foundation model trained only on secure code.

Question 4

A data scientist is fine-tuning a foundation model on SageMaker. They want to prevent overfitting. Which THREE actions can help? (Select THREE.)

Accepted Answer

Apply dropout. Option A is correct because dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and reduces overfitting. In SageMaker, dropout can be applied via framework-specific APIs (e.g., `tf.keras.layers.Dropout` in TensorFlow) or by configuring the model architecture in the training script.

Answer

Increase the number of epochs

Answer

Use a smaller learning rate

Question 5

Refer to the exhibit. The training job is failing with an error 'CUDA out of memory'. Which hyperparameter change is MOST likely to resolve the issue?

Accepted Answer

Reduce per_device_train_batch_size to 4. The 'CUDA out of memory' error indicates that the GPU memory is exhausted during training. Reducing `per_device_train_batch_size` directly decreases the number of samples processed simultaneously per GPU, which lowers memory consumption for activations, gradients, and optimizer states. This is the most direct and effective hyperparameter change to resolve an out-of-memory condition.

Answer

Increase the number of epochs to 10

Answer

Increase learning_rate to 5e-4

Answer

Increase max_seq_length to 1024

Question 6

Which parameter controls the randomness of generated text in a foundation model?

Accepted Answer

temperature. Temperature is the correct parameter because it directly controls the randomness of token sampling in a foundation model. A lower temperature (e.g., 0.1) makes the model more deterministic by concentrating probability mass on the most likely tokens, while a higher temperature (e.g., 1.5) flattens the probability distribution, increasing the likelihood of less probable tokens and thus generating more diverse or creative outputs.

Answer

top_p

Answer

stop sequences

Answer

max_tokens

Question 7

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.

Answer

Use a different foundation model

Answer

Add more positive examples to training data

Answer

Increase training epochs

Question 8

Which THREE are best practices for ensuring generated content complies with corporate brand guidelines when using Amazon Bedrock?

Accepted Answer

Implement guardrails to restrict tone, topics, and language. Option A is correct because Amazon Bedrock Guardrails allow you to define policies that restrict the model's output to specific tones, topics, and language, ensuring alignment with corporate brand guidelines. By configuring denied topics and content filters, you can prevent the model from generating off-brand or inappropriate content, directly enforcing compliance at the inference layer.

Answer

Increase the temperature for more creative outputs

Answer

Use random prompts to test variability

Question 9

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.

Answer

Amazon CloudWatch

Answer

AWS CloudTrail

Answer

Amazon Athena

Question 10

A data scientist wants to quickly experiment with a pre-trained LLM for text generation without writing any code. Which AWS service is MOST suitable?

Accepted Answer

Amazon SageMaker JumpStart. Amazon SageMaker JumpStart provides a curated set of pre-trained foundation models (including LLMs) that can be deployed with just a few clicks, requiring no code. This makes it the most suitable service for a data scientist who wants to quickly experiment with a pre-trained LLM for text generation without writing any code.

Answer

Amazon Bedrock

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Amazon EC2

Answer

AWS Lambda

Question 11

Which TWO AWS services can be used together to build a chatbot that leverages a foundation model for natural language understanding?

Accepted Answer

Amazon Lex. Amazon Lex provides the conversational interface and natural language understanding (NLU) to interpret user intents and manage dialog, while Amazon Bedrock gives access to foundation models (FMs) for advanced natural language generation and understanding. Together, Lex can route utterances to a Bedrock FM via a Lambda function or direct integration, enabling a chatbot that leverages a pre-trained FM for richer responses.

Answer

Amazon Rekognition

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Amazon Polly

Answer

AWS Glue

Question 12

Which TWO actions are best practices when deploying foundation models on Amazon SageMaker for production? (Choose TWO.)

Accepted Answer

Use multi-model endpoints (MMEs) to serve multiple models on a single instance.. Option C is correct because Amazon SageMaker Multi-Model Endpoints (MMEs) allow you to host multiple models on a single instance, which reduces hosting costs by sharing resources across models while still providing low-latency inference. This is a best practice for production deployments where you need to serve many models efficiently without provisioning separate endpoints for each.

Answer

Manually warm up endpoints by sending dummy requests before traffic spikes.

Answer

Create a separate endpoint for each model to isolate traffic.

Answer

Deploy models directly to production without load testing to avoid delays.

Question 13

A company uses Amazon Bedrock to generate product descriptions. They need to ensure outputs do not contain offensive language. Which service should they integrate to filter content?

Accepted Answer

Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is specifically designed to enforce content policies for foundation model outputs, including filtering for offensive language, hate speech, and other harmful content. It integrates directly with Bedrock to apply customizable safety filters and deny topics without requiring additional services or custom code.

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Answer

AWS WAF

Question 14

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.

Answer

The model was fine-tuned

Answer

The model is not the latest version

Answer

The inference parameters are incorrect

Question 15

Which THREE are benefits of using Amazon Bedrock over self-managing foundation models on EC2? (Choose THREE.)

Accepted Answer

Built-in integration with AWS services such as AWS CloudWatch and AWS CloudTrail.. Option A is correct because Amazon Bedrock provides built-in integration with AWS services like CloudWatch for monitoring model invocation metrics and CloudTrail for auditing API calls. This eliminates the need to manually set up logging and monitoring infrastructure when self-managing foundation models on EC2, where you would have to configure these integrations yourself.

Answer

Lower data transfer costs between cloud regions.

Answer

Greater control over model fine-tuning and customization.

Question 16

A developer is building a RAG-based Q&A bot with Amazon Bedrock Knowledge Bases. They need a managed vector store for document embeddings. Which service should they use?

Accepted Answer

Amazon OpenSearch Serverless. Amazon Bedrock Knowledge Bases requires a vector store to store and query document embeddings for Retrieval-Augmented Generation (RAG). Amazon OpenSearch Serverless provides a managed, scalable vector engine that supports k-NN (k-nearest neighbor) search, making it the correct choice for this use case. It integrates natively with Bedrock Knowledge Bases to handle embedding storage and similarity search without manual infrastructure management.

Answer

Amazon DynamoDB

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Amazon RDS

Answer

Amazon S3

Question 17

A company uses Amazon Bedrock to generate code snippets for internal tools. They notice that the generated code often contains security vulnerabilities such as SQL injection and cross-site scripting. The security team has compiled a comprehensive list of secure coding guidelines and examples of vulnerable patterns. The development team wants to reduce vulnerabilities without significantly slowing down the code generation process. They have tried adding the guidelines to the system prompt, but the model still produces insecure code occasionally. The team is considering additional measures. Which action should they take to most effectively eliminate security vulnerabilities in the generated code?

Accepted Answer

Implement a post-processing step using Amazon CodeGuru or a similar static analysis tool to scan the generated code for vulnerabilities and reject or fix insecure code.. Option A is correct because it introduces a deterministic, post-generation validation layer that catches vulnerabilities the model might miss. Amazon CodeGuru Reviewer or similar static analysis tools can scan generated code for patterns like SQL injection and XSS, then reject or fix insecure code without modifying the generation process itself. This approach directly addresses the security team's guidelines while maintaining generation speed, as the model's inference latency is unaffected.

Answer

Use a larger, more expensive foundation model that specializes in code generation.

Answer

Include the complete secure coding guidelines in every prompt.

Answer

Increase the temperature parameter of the foundation model to promote more diverse outputs.

Question 18

A developer wants to experiment with a foundation model for code generation without writing any code. Which AWS service provides a playground for models like CodeWhisperer?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is the correct answer because it provides a web-based interface for experimenting with foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon itself, including the CodeWhisperer model for code generation. This allows the developer to test prompts, adjust parameters, and see model responses without writing any code, directly fulfilling the requirement of a no-code playground.

Answer

Amazon CodeGuru

Answer

AWS Lambda

Answer

Amazon SageMaker Studio

Question 19

A data science team is fine-tuning a Llama 2 7B model on Amazon SageMaker for a text classification task. After the first training run, they notice the loss is not decreasing and the model is overfitting to the small training set. What should the team change to mitigate overfitting?

Accepted Answer

Add dropout layers and reduce the learning rate.. Option A is correct because adding dropout layers introduces regularization by randomly dropping neurons during training, which prevents the model from relying too heavily on specific features and reduces overfitting. Reducing the learning rate helps the model converge more smoothly and avoid oscillating around a suboptimal minimum, which is especially important when fine-tuning a large model like Llama 2 7B on a small dataset. Together, these changes address the core issues of overfitting and non-decreasing loss.

Answer

Increase the number of epochs to allow the model to learn more patterns.

Answer

Increase the batch size and use gradient accumulation.

Answer

Remove dropout layers from the model architecture.

Question 20

Refer to the exhibit. A developer sees this error when calling Amazon Bedrock for inference. What is the MOST likely cause and recommended solution?

Accepted Answer

The request rate exceeds the model's throughput limit; implement retries with exponential backoff. The error indicates a throttling exception from Amazon Bedrock, which occurs when the request rate exceeds the model's throughput limit. The recommended solution is to implement retries with exponential backoff to handle transient rate limits gracefully, as this aligns with AWS best practices for managing API call limits.

Answer

The model ID is incorrect; use a different model

Answer

The prompt is too long; reduce the number of tokens in the prompt

Answer

Increase the max_tokens_to_sample value