AIF-C01 Question Bank · Latest 50 Questions Free Preview (Page 2/5)
Questions 11–20 of the latest 50 AIF-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 435 questions online.
Question 11 · No. 396 · Single choice
A company stores its AI datasets in Amazon S3 buckets. The company wants to share the S3 buckets with its business partners. The company needs to avoid accidentally sharing sensitive data. Which AWS service should the company use to discover sensitive data in the dataset?
- A. Amazon Kendra
- B. Amazon Macie
- C. Amazon Textract
- D. AWS Data Exchange
Answer: B
Explanation: Amazon Macie is a fully managed data security and privacy service that uses machine learning and pattern matching to discover, classify, and protect sensitive data in AWS. It is specifically designed to scan S3 buckets for sensitive information like personally identifiable information (PII), financial data, and credentials. Amazon Kendra is a search service, Amazon Textract extracts text from documents, and AWS Data Exchange is for sharing and subscribing to third-party data products. None of these are designed for sensitive data discovery.
Question 12 · No. 397 · Single choice
A company is using AI to build a toy recommendation website that suggests toys based on a customer’s interests and age. The company notices that the AI tends to suggest stereotypically gendered toys. Which AWS service or feature should the company use to investigate the bias?
- A. Amazon Rekognition
- B. Amazon Q Developer
- C. Amazon Comprehend
- D. Amazon SageMaker Clarify
Answer: D
Explanation: Amazon SageMaker Clarify is specifically designed to detect and measure bias in machine learning models and datasets. It provides capabilities to identify imbalances in data and model predictions across sensitive attributes such as gender, age, and race. In this scenario, where the AI toy recommendation system is exhibiting gender bias, SageMaker Clarify is the appropriate tool to investigate and quantify the bias. The other options are not suitable: Amazon Rekognition is for image/video analysis, Amazon Q Developer is a coding assistant, and Amazon Comprehend is for NLP tasks.
Question 13 · No. 398 · Single choice
Which option is a disadvantage of using generative AI models in production systems?
- A. Possible high accuracy and reliability
- B. Deterministic and consistent behavior
- C. Negligible computational resource requirements
- D. Hallucinations and inaccuracies
Answer: D
Explanation: Generative AI models can produce hallucinations and inaccuracies, which is a significant disadvantage in production systems because unreliable outputs may lead to incorrect decisions or actions.
Question 14 · No. 399 · Single choice
Which AWS service helps select foundation models (FMs) for generative AI use cases?
- A. Amazon Personalize
- B. Amazon Bedrock
- C. Amazon Q Developer
- D. Amazon Rekognition
Answer: B
Explanation: Amazon Bedrock provides a selection of foundation models from various providers, allowing users to choose models for generative AI use cases.
Question 15 · No. 400 · Single choice
A company wants to increase employee productivity by using a generative AI solution to write code to test software applications. Which solution will meet these requirements with the LEAST operational effort?
- A. Amazon Q Business
- B. Amazon Bedrock Agents
- C. Amazon Q Developer
- D. Amazon SageMaker Clarify
Answer: C
Explanation: Amazon Q Developer is specifically designed to assist developers with coding tasks, including writing code to test software applications. It is a fully managed service that requires the least operational effort compared to the other options. Amazon Q Business is for business insights, Amazon Bedrock Agents requires more configuration, and Amazon SageMaker Clarify is for ML bias detection.
Question 16 · No. 401 · Single choice
A company wants to develop an AI solution to automatically extract structured and unstructured data from scanned PDF documents and forms. Which AWS service will meet these requirements?
- A. Amazon Polly
- B. Amazon Lex
- C. Amazon Personalize
- D. Amazon Textract
Answer: B
Question 17 · No. 402 · Single choice
Which AWS service provides a broad range of high-performing generative AI foundation models (FMs)?
- A. Amazon Bedrock
- B. Amazon Rekognition
- C. Amazon Lex
- D. Amazon Kendra
Answer: A
Explanation: Amazon Bedrock is the AWS service that provides a broad range of high-performing generative AI foundation models (FMs). It offers access to foundation models from leading AI companies such as Anthropic (Claude), AI21 Labs (Jurassic), Stability AI (Stable Diffusion), and Amazon's own Titan models, all through a single API. The other options serve different purposes: Amazon Rekognition is for image and video analysis, Amazon Lex is for building conversational chatbots, and Amazon Kendra is an intelligent enterprise search service powered by machine learning.
Question 18 · No. 403 · Single choice
A company is using Retrieval Augmented Generation (RAG) in its generative AI (GenAI) application. The company wants to use Amazon Bedrock Knowledge Bases evaluations to measure how well responses avoid hallucination based on the retrieved texts. Which metric meets these requirements?
- A. Refusal
- B. Harmfulness
- C. Stereotyping
- D. Faithfulness
Answer: D
Explanation: Faithfulness is the correct metric because it specifically measures whether the model's response is grounded in and consistent with the retrieved source documents. When a response is faithful to the retrieved texts, it means the model is not fabricating or hallucinating information beyond what is supported by the source material. The other options measure different concerns: Refusal checks if the model appropriately declines to answer certain questions, Harmfulness evaluates if responses contain harmful content, and Stereotyping detects biased content. None of these directly address hallucination based on retrieved texts.
Question 19 · No. 404 · Multiple choice
A global logistics company is using generative AI (GenAI) models on Amazon Bedrock to automate paperwork and shipment documentation. The company wants to set up a clear AI governance process to make sure the models are used responsibly and meet company policies. Which solutions will meet these requirements? (Choose two.)
- A. Set up a regular review process to check that the models' results are accurate and fair and follow company policies.
- B. Provide training for all employees who work with AI tools so the employees understand company rules, data handling, and responsible AI use.
- C. Let the cloud provider manage all AI safety checks and reviews to save internal effort.
- D. Involve only developers in governance reviews because the developers build and deploy the AI systems.
- E. Review the models only when users report problems or errors in the system.
Answer: A, B
Explanation: Options A and B are the correct answers for establishing effective AI governance. Option A establishes a regular review process to ensure model outputs are accurate, fair, and compliant with company policies, which is a core requirement for responsible AI governance. Option B provides training for all employees working with AI tools, ensuring they understand company rules, data handling practices, and responsible AI use, which is essential for maintaining governance standards across the organization. Options C, D, and E are incorrect because: delegating all AI safety checks to the cloud provider (C) removes critical company responsibility and oversight; limiting governance reviews to only developers (D) ignores the cross-functional nature of AI governance which requires diverse stakeholder input; and reviewing models only when problems are reported (E) is reactive rather than proactive, missing the continuous monitoring needed for responsible AI deployment.
Question 20 · No. 405 · Single choice
A company wants to call a single Amazon Bedrock endpoint and needs Amazon Bedrock to pick the most suitable foundation model (FM) for each request. The company does not want to hardcode a model. Which Amazon Bedrock feature or resource will meet these requirements?
- A. Amazon Bedrock Guardrails
- B. Amazon Bedrock Knowledge Bases
- C. Amazon Bedrock prompt router
- D. Amazon Bedrock agent memory
Answer: C
Explanation: Amazon Bedrock prompt router is the feature designed to meet these requirements. It allows applications to send requests to a single endpoint, and Bedrock will automatically analyze the prompt and route it to the most suitable foundation model from a configured set of models. This eliminates the need to hardcode a specific model in the application code. The other options serve different purposes: Guardrails (A) is for safety and compliance controls, Knowledge Bases (B) is for RAG with custom data, and agent memory (D) is for storing conversation context for agents.