AIF-C01 Applications of Foundation Models Practice Test 1 — 15 Questions

Question 1

A healthcare company is using Amazon Bedrock to summarize patient notes. The compliance team requires that no patient data is used to improve the underlying foundation model. Which configuration should the team choose?

Accepted Answer

Disable model training data logging in the AWS console.. Option C is correct because disabling model training data logging in the AWS console prevents Amazon Bedrock from using customer inference data to improve the underlying foundation model. This setting ensures compliance with the requirement that no patient data is used for model training, as Bedrock offers a specific toggle to opt out of data sharing for model improvement.

Answer

Enable data encryption in transit and at rest.

Answer

Use a different foundation model from a different provider.

Answer

Configure a VPC endpoint for Amazon Bedrock.

Question 2

A marketing firm uses Amazon Bedrock to generate ad copy. They notice that the generated text often includes factual inaccuracies about their products. Which technique would most effectively reduce these inaccuracies?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a product knowledge base.. Retrieval-Augmented Generation (RAG) grounds the model's output in a trusted, external knowledge base by retrieving relevant product documents before generating text. This directly addresses factual inaccuracies because the model references authoritative data rather than relying solely on its parametric memory, which may contain outdated or incorrect information.

Answer

Use longer, more detailed prompts.

Answer

Increase the temperature parameter to 0.9.

Answer

Fine-tune the model on a dataset of previous ad copies.

Question 3

A developer is using Amazon Bedrock to build a chatbot that answers customer queries. The chatbot must only respond based on the provided company documentation. Which approach best meets this requirement?

Accepted Answer

Use a RAG architecture with the company documentation as the knowledge base.. Option B is correct because Retrieval-Augmented Generation (RAG) architecture retrieves relevant chunks from the company documentation at query time and injects them into the prompt, ensuring the model's response is grounded solely in the provided documents. This approach prevents the model from relying on its internal training data or generating information outside the documentation, which is critical for a closed-domain chatbot.

Answer

Use prompt engineering to instruct the model to only use documentation.

Answer

Fine-tune a foundation model on the company documentation.

Answer

Use a text classification model to filter responses.

Question 4

A financial services company is deploying a foundation model to analyze customer sentiment from call transcripts. The model outputs must be consistent and deterministic for auditing purposes. Which parameter configuration should the company use?

Accepted Answer

Set temperature to 0 and top_p to 1.. Setting temperature to 0 and top_p to 1 forces the model to always select the highest-probability token at each step, producing deterministic and repeatable outputs. This is essential for auditing and compliance in financial services, where consistency is required. Any nonzero temperature introduces randomness, which undermines determinism.

Answer

Set temperature to 0.1 and top_p to 0.9.

Answer

Set temperature to 0.7 and top_p to 1.0.

Answer

Set temperature to 0.5 and top_p to 0.5.

Question 5

An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?

Accepted Answer

Amazon ElastiCache. Amazon ElastiCache is the correct choice because it provides an in-memory caching layer (using Redis or Memcached) that can store frequently requested product descriptions, reducing the need to invoke the foundation model repeatedly. This directly lowers inference costs and latency by serving cached responses instead of generating new ones each time.

Answer

Amazon CloudFront

Answer

Amazon DynamoDB

Answer

Amazon S3

Question 6

A company wants to use a foundation model to automatically moderate user-generated content. The model must filter out inappropriate content with high accuracy. Which Amazon service is best suited for this task?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is the correct choice because it is a natural language processing (NLP) service that can analyze text for sentiment, key phrases, and — critically — toxicity and inappropriate content using built-in or custom classifiers. This directly matches the requirement to moderate user-generated text with high accuracy, as it can detect hate speech, profanity, and other harmful language.

Answer

Amazon Translate

Answer

Amazon Rekognition

Answer

Amazon Polly

Question 7

A startup is using Amazon Bedrock to power a virtual assistant. They need to ensure that personally identifiable information (PII) is not included in the model's responses. Which feature should they enable?

Accepted Answer

Enable PII redaction in the Bedrock guardrails.. Amazon Bedrock Guardrails provide a configurable content filtering and PII redaction feature that can automatically detect and mask personally identifiable information (PII) in model inputs and outputs. By enabling PII redaction within guardrails, the startup can ensure that sensitive data like names, addresses, or credit card numbers are removed or obfuscated before the virtual assistant's responses reach the user. This is the direct and intended mechanism for preventing PII leakage in model responses.

Answer

Enable model invocation logging.

Answer

Configure a VPC endpoint.

Answer

Enable data encryption at rest.

Question 8

A company is using Amazon Bedrock to generate marketing content. They want to evaluate the quality of the generated text. Which TWO metrics are most appropriate for evaluating text quality?

Accepted Answer

Perplexity. Perplexity measures how well a language model predicts a sample, with lower values indicating higher confidence and coherence in generated text. BLEU evaluates the overlap between generated text and reference text, making it suitable for assessing fluency and relevance in content generation tasks like marketing copy.

Answer

Precision

Answer

Accuracy

Answer

F1 score

Question 9

A data scientist is fine-tuning a foundation model on Amazon Bedrock for a custom summarization task. Which THREE practices should they follow to optimize the fine-tuning process?

Accepted Answer

Start with a base model that is already strong in the domain.. Starting with a base model that is already strong in the domain (Option A) is correct because it reduces the amount of fine-tuning data and compute required. Amazon Bedrock provides access to various foundation models (e.g., Anthropic Claude, Amazon Titan) that have been pre-trained on diverse corpora; selecting one that is already proficient in the target domain (e.g., legal or medical summarization) means the model's existing knowledge can be adapted with fewer training steps, leading to better performance and lower risk of catastrophic forgetting.

Answer

Use the default hyperparameters without tuning.

Answer

Train for as many epochs as possible.

Question 10

A company is using Amazon Bedrock to generate code snippets. They want to ensure the generated code is secure. Which TWO practices should they implement?

Accepted Answer

Use guardrails to block insecure code patterns.. Option B is correct because Amazon Bedrock Guardrails allow you to define policies that filter or block generated content containing insecure code patterns, such as SQL injection or hardcoded credentials, before the output is returned. This provides a proactive security layer that prevents insecure code from reaching the user, directly addressing the requirement to ensure generated code is secure.

Answer

Increase the max token limit to generate longer code.

Answer

Set the temperature to 0 for deterministic output.

Answer

Use a larger model for better accuracy.

Question 11

Refer to the exhibit. An IAM policy is attached to a user. Which models can the user invoke?

Accepted Answer

Only Claude v2. The IAM policy explicitly allows the `bedrock:InvokeModel` action only on the resource ARN `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`. This means the user can invoke only the Claude v2 model. No other models, including other Claude versions or any model with 'claude' in its name, are permitted because the resource ARN is exact and does not use wildcards.

Answer

No models

Answer

Claude v2 and any model with a name containing 'claude'

Answer

Any model in the account

Question 12

Refer to the exhibit. A user invokes Claude v2 using the AWS CLI. The response is truncated. What is the most likely cause?

Accepted Answer

The prompt includes a stop sequence 'Assistant:'.. Option D is correct because the prompt includes the stop sequence 'Assistant:', which causes the model to halt generation as soon as it encounters that token sequence. In Claude v2, stop sequences are used to control the output length and structure; when the model generates the exact stop sequence, it truncates the response at that point, even if more content could have been produced.

Answer

The AWS CLI is missing the --endpoint-url parameter.

Answer

The max_tokens_to_sample is too low.

Answer

The model does not support this use case.

Question 13

A company is building a chatbot using Amazon Bedrock to answer customer questions about their product catalog. The chatbot should only use information from the company's internal knowledge base and should not generate answers based on the model's pre-training data. Which feature should be enabled?

Accepted Answer

Configure a knowledge base with Retrieval Augmented Generation (RAG). Option B is correct because configuring a knowledge base with Retrieval Augmented Generation (RAG) allows the chatbot to retrieve relevant documents from the company's internal knowledge base and use them as context for generating answers. This ensures the model's responses are grounded solely in the provided data, preventing reliance on its pre-training knowledge.

Answer

Use prompt engineering to instruct the model to only use the knowledge base

Answer

Enable model invocation logging to review responses

Answer

Fine-tune the model on the product catalog data

Question 14

A financial services company is using Amazon Bedrock to generate investment summaries. They want to ensure that the model outputs are factually accurate and based on the latest market data. Which combination of services should they use to achieve this? (Select TWO)

Accepted Answer

Amazon Aurora with the pgvector extension. Amazon Aurora with the pgvector extension (Option D) enables storing and querying vector embeddings directly within a PostgreSQL-compatible database, which is essential for Retrieval-Augmented Generation (RAG). When combined with Amazon Bedrock Knowledge Bases (Option E), it allows the company to retrieve the most current market data as vector embeddings, ensuring the generated investment summaries are grounded in factual, up-to-date information rather than relying solely on the model's static training data.

Answer

Amazon SageMaker Ground Truth for data labeling

Answer

Amazon DynamoDB as the knowledge base store

Answer

Amazon Kendra for indexing the knowledge base

Question 15

A company is using a foundation model on Amazon Bedrock to generate customer support responses. They notice that the model sometimes produces harmful or offensive content. Which approach is MOST effective to mitigate this issue?

Accepted Answer

Configure Amazon Bedrock Guardrails with content filters. Amazon Bedrock Guardrails provides configurable content filters that can block harmful, offensive, or inappropriate content in both user inputs and model outputs. This is the most effective approach because it operates at the inference layer, applying safety policies consistently across all requests without requiring model retraining or manual review. Prompt engineering alone is unreliable, and fine-tuning may not generalize to all harmful content patterns.

Answer

Use prompt engineering to instruct the model to avoid harmful content

Answer

Enable model invocation logging to review and block responses

Answer

Fine-tune the model on a curated dataset of safe responses