AIF-C01 Generative AI and Foundation Models Practice Test 5 — 15 Questions

Question 1

A company is building a real-time document analysis tool using Amazon Bedrock. Their documents average 15,000 tokens each. Users submit a document and ask a single question about it. The team wants to minimize latency while maintaining answer quality. Which approach is MOST suitable?

Accepted Answer

Use Amazon Bedrock's Converse API with a prompt that includes the full document text and the user's question in a single invocation.. Option C is correct because using Amazon Bedrock's Converse API with a prompt that includes the full document text and the user's question in a single invocation minimizes latency by avoiding the overhead of multiple API calls or retrieval steps. This approach maintains answer quality because the model has access to the entire document context (up to 15,000 tokens) in one pass, which is well within the context window of models like Claude 3 or Llama 2, ensuring accurate responses without the need for chunking or external retrieval.

Answer

Use Amazon Bedrock's Knowledge Base (RAG) to retrieve relevant chunks from the document and then answer the question.

Answer

Split the document into chunks of 1,000 tokens each, process each chunk with the model separately, and aggregate results.

Answer

Use a fine-tuned model that has been trained on similar documents to avoid context processing.

Question 2

A data scientist is using Amazon Bedrock to generate product descriptions. They want to ensure the output is creative but still relevant. Which parameter adjustment would MOST directly control the randomness of the model's responses?

Question 3

Which of the following is a key benefit of using foundation models over training a model from scratch for natural language tasks?

Question 4

A developer is using the Amazon Bedrock Converse API to build a multi-turn chatbot. They notice that after several exchanges, the model starts to forget earlier context. What is the MOST likely cause?

Question 5

A company needs to generate high-quality images from text descriptions for a marketing campaign. They need to ensure the images are photorealistic and that the model can generate variations of a given image. Which type of model should they use?

Question 6

What is the primary purpose of an embedding model in the context of semantic search?

Question 7

A company uses Amazon Bedrock with Anthropic Claude. They want to generate code explanations that include step-by-step reasoning. Which prompt engineering technique is BEST suited for this?

Question 8

A startup is building a medical diagnosis assistant. They have a small dataset of doctor-patient conversations. Which approach should they take to minimize cost while ensuring the model understands medical terminology?

Question 9

Which component of the Transformer architecture allows the model to weigh the importance of different words in a sentence when generating output?

Accepted Answer

Self-attention mechanism. The self-attention mechanism in the Transformer architecture computes attention scores between every pair of words in the input sequence, allowing the model to dynamically weigh the importance of each word when generating output. This is achieved through scaled dot-product attention, where query, key, and value matrices are used to produce context-aware representations.

Question 10

A company is using Amazon Bedrock to build a chatbot that must comply with data residency regulations. All data must remain in a specific AWS region. Which action is MOST important to meet this requirement?

Question 11

A developer needs to choose a model on Amazon Bedrock for a text summarization task. The summaries must be accurate and concise, and the input documents are up to 5,000 tokens. Which model selection criteria should be prioritized?

Question 12

What is the main advantage of using the Amazon Bedrock Converse API over the InvokeModel API for building conversational applications?

Accepted Answer

The Converse API provides a consistent interface that abstracts model-specific differences, simplifying conversational workflows.. The Amazon Bedrock Converse API is specifically designed for conversational applications, providing a consistent interface that abstracts away the differences between various foundation models. Unlike InvokeModel, which requires you to handle model-specific request and response formats manually, Converse API standardizes message history, turn-taking, and tool use, significantly reducing development complexity.

Answer

The Converse API provides faster inference than the InvokeModel API.

Answer

The Converse API supports a larger selection of foundation models than the InvokeModel API.

Answer

The Converse API is less expensive than the InvokeModel API due to batch processing capabilities.

Question 13

A company is building a legal document search system. They need to find relevant documents based on natural language queries. Which TWO AWS services or features should they combine to implement this? (Select TWO.)

Question 14

A developer is using the Amazon Bedrock InvokeModel API with streaming enabled. They want to process partial results as they arrive. Which THREE steps are necessary to implement streaming correctly? (Select THREE.)

Question 15

A company wants to use Amazon Bedrock to generate product images. They need to control the style (e.g., watercolor, oil painting) and ensure the images are safe for work (no inappropriate content). Which TWO features should they use? (Select TWO.)