Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 201:

  A company wants to use large language models (LLMs) to create a chatbot. The chatbot will assist customers with product inquiries, order tracking, and returns. The chatbot must be able to process text inputs and image inputs to generate responses.

  Which AWS service meets these requirements?

  A. Amazon Bedrock
  B. Amazon Comprehend
  C. Amazon Q
  D. Amazon Rekognition
  A. Amazon Bedrock

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  Explanation

  Amazon Bedrock provides access to multimodal large language models that can process both text and image inputs and generate intelligent responses, making it suitable for building a chatbot that handles product inquiries, order tracking, returns, and visual understanding in a single generative AI solution.

• Question 202:

  Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?

  A. RAG can use external knowledge sources to generate more accurate and informative responses.
  B. RAG is designed to improve the speed of language model training.
  C. RAG is primarily used for speech recognition tasks.
  D. RAG is a technique for data augmentation in computer vision tasks.
  A. RAG can use external knowledge sources to generate more accurate and informative responses.

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  By retrieving relevant documents or data at inference time and conditioning the generation on that external knowledge, RAG enriches model outputs with up-to-date, domain-specific information, boosting accuracy and informativeness without retraining the core model.

• Question 203:

  A company maintains a large product catalog with detailed descriptions. The company wants to build an AI assistant to answer customer questions about the products. However, the company's labeled training data is limited.

  Which solution will meet these requirements with the LEAST implementation effort?

  A. Use a Retrieval Augmented Generation (RAG) architecture to query the product database at runtime. Provide relevant, unmodified product descriptions as context for the foundation model (FM).
  B. Fine-tune a foundation model (FM) on the restricted labeled data. Automatically refresh the model with novel product details weekly for accuracy.
  C. Deploy a foundation model (FM) for each product category. Implement a routing layer to direct customer queries to the appropriate specialized model.
  D. Create a new custom foundation model (FM) that is trained on the product database. Optimize the FM for minimal token usage during inference.
  A. Use a Retrieval Augmented Generation (RAG) architecture to query the product database at runtime. Provide relevant, unmodified product descriptions as context for the foundation model (FM).

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  Explanation

  A RAG architecture is the least-effort solution because it uses the existing product catalog as the knowledge source at runtime, so the foundation model can answer questions with current product information without requiring extensive labeled data or custom model training.

• Question 204:

  Which ML technique ensures data compliance and privacy when training AI models on AWS?

  A. Reinforcement learning
  B. Transfer learning
  C. Federated learning
  D. Unsupervised learning
  C. Federated learning

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  Federated learning lets you train a global model across multiple data holders (for example, different AWS accounts or edge devices) without moving raw data to a central location. Each participant trains locally on its own private dataset and only shares model updates, preserving data privacy and ensuring compliance.

• Question 205:

  A company is building a customer support workflow that requires a foundation model to gather information, decide what action to take next, and call downstream systems in multiple steps.

  Which approach best fits this requirement?

  A. Use agents for multi-step task orchestration
  B. Use only image generation models
  C. Use batch inference only
  D. Use Amazon CloudFront caching only
  A. Use agents for multi-step task orchestration

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  Explanation

  Agents for multi-step task orchestration is the correct answer because agents are designed to coordinate multiple actions and decisions across a workflow rather than producing a single standalone response.

  Option A (Correct): "Use agents for multi-step task orchestration": This is correct because agents can manage complex tasks that require several coordinated steps.

  Option B: "Use only image generation models" is incorrect because the problem is workflow orchestration, not image creation.

  Option C: "Use batch inference only" is incorrect because batch processing does not coordinate real-time multistep decisions.

  Option D: "Use Amazon CloudFront caching only" is incorrect because caching does not perform workflow logic.

• Question 206:

  A company uses an Amazon Bedrock large language model (LLM) in an application. During testing, the company observes different outputs from the same input.

  What is the MOST likely cause of this issue?

  A. The LLM is acting in a nondeterministic way.
  B. The guardrails of the LLM are not configured properly.
  C. The LLM has security vulnerabilities.
  D. The LLM is acting in a deterministic way.
  A. The LLM is acting in a nondeterministic way.

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  Explanation

  LLMs often generate outputs nondeterministically, meaning the same input can produce different responses due to inherent randomness in the generation process.

• Question 207:

  A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost. Which solution will meet these requirements?

  A. Customize the model by using fine-tuning.
  B. Decrease the number of tokens in the prompt.
  C. Increase the number of tokens in the prompt.
  D. Use Provisioned Throughput.
  B. Decrease the number of tokens in the prompt.

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  Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

• Question 208:

  An AI Practitioner is using an LLM-as-a-judge in Amazon Bedrock to evaluate the quality of agent responses in a production environment. The AI practitioner wants to apply a built-in metric that assesses. how thoroughly the agent responses address all parts of each prompt or question.

  Which metric will meet these requirements?

  A. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  B. Completeness
  C. Following instructions
  D. Refusal
  B. Completeness

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  Explanation

  The completeness metric evaluates how fully an agent's response addresses all parts and requirements of a user's prompt, making it the correct built-in metric for assessing thorough coverage of each question or request.

• Question 209:

  A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost. Which combination of AWS service and storage class meets these requirements? (Choose two.)

  A. AWS CloudTrail
  B. Amazon CloudWatch
  C. AWS Audit Manager
  D. Amazon S3 Intelligent-Tiering
  E. Amazon S3 Standard
  A. AWS CloudTrail
  D. Amazon S3 Intelligent-Tiering

• Question 210:

  A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base. Which technique should the company use to optimize the generated responses?

  A. Use Retrieval Augmented Generation (RAG).
  B. Use few-shot prompting.
  C. Set the temperature to 1.
  D. Decrease the token size.
  A. Use Retrieval Augmented Generation (RAG).

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  RAG lets the chatbot pull in precise, up-to-date passages from your HR documentation at inference time, grounding its answers in the actual policy text and ensuring accuracy without overloading the LLM's context window.