Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 251:

  Which AWS service makes foundation models (FMs) available to help users build and scale generative AI applications?

  A. Amazon Q Developer
  B. Amazon Bedrock
  C. Amazon Kendra
  D. Amazon Comprehend
  B. Amazon Bedrock

  ---

  Amazon Bedrock is an AWS service that allows users to build and scale generative AI applications using foundation models (FMs) without the need to manage infrastructure. It provides access to pre-trained models from AWS and third-party providers, enabling developers to integrate generative AI capabilities like text generation, summarization, and question-answering into their applications. This makes Amazon Bedrock the ideal choice for building and scaling generative AI solutions.

• Question 252:

  A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams. Which SageMaker feature meets these requirements?

  A. Amazon SageMaker Feature Store
  B. Amazon SageMaker Data Wrangler
  C. Amazon SageMaker Clarify
  D. Amazon SageMaker Model Cards
  A. Amazon SageMaker Feature Store

  ---

  Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.

• Question 253:

  An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text- based chatbot interface. Which solution will meet these requirements with the LEAST development effort?

  A. Train models on Amazon SageMaker Autopilot.
  B. Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.
  C. Create a Python application by using Amazon Q Developer.
  D. Fine-tune models on Amazon SageMaker Jumpstart.
  B. Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.

  ---

  Amazon Bedrock allows quick integration of large language models with a knowledge base using RAG architecture, enabling accurate and dynamic responses based on up-to-date information. This approach requires minimal development effort while leveraging powerful generative capabilities.

• Question 254:

  A research company is growing microbiological cultures. The company captures images of the cultures without any prior labeled data about growth areas. The company needs to identify the regions of the images that show culture growth.

  Which ML technique will meet these requirements?

  A. Logistic regression
  B. Decision tree
  C. Clustering
  D. Dimensionality reduction
  C. Clustering

  ---

  Explanation

  Clustering can group similar pixel patterns or image regions without labeled data, allowing the model to identify areas that share visual characteristics associated with culture growth.

• Question 255:

  A company wants to allow employees to privately access Amazon Bedrock models only through the corporate VPC and without internet exposure. Which solution meets this requirement?

  A. Use AWS CloudTrail
  B. Use AWS PrivateLink
  C. Use an Amazon S3 VPC endpoint
  D. Deploy EC2 gateway endpoints
  B. Use AWS PrivateLink

  ---

  AWS PrivateLink enables private, internal-only connectivity to Bedrock APIs, ensuring no traffic traverses the public internet. This is essential for secure enterprise deployments.

• Question 256:

  HOTSPOT

  A company is training its employees on how to structure prompts for foundation models.

  Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected one time.

  

  

  Explanation:

  "Classify the following text as either sports, politics, or entertainment: [input text]."

  Correct Answer: Zero-shot learning

  Zero-shot learning involves providing the model with a task and no prior examples, relying entirely on the model's pre-trained knowledge to perform the classification. "A [image 1], [image 2], and [image 3] are examples of [target class]. Classify the following image as [target class]."

  Correct Answer: Few-shot learning

  Few-shot learning provides the model with a few examples (image 1, image 2, and image 3) before asking it to classify a new instance, helping it generalize to the task with minimal examples.

  "[Question.] [Instructions to follow.] Think step by step and walk me through your thinking."

  Correct Answer: Chain-of-thought reasoning

  Chain-of-thought reasoning encourages the model to break down its reasoning process step by step, enhancing its ability to solve complex tasks logically.

• Question 257:

  A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair. Which solution meets these requirements?

  A. Review the training data to check for biases. Include data from all demographics in the training data.
  B. Use a deep learning model with many hidden layers.
  C. Keep the model's decision-making process a secret to protect proprietary algorithms.
  D. Continuously monitor the model's performance on a static test dataset
  A. Review the training data to check for biases. Include data from all demographics in the training data.

  ---

  Reviewing the training data for biases and ensuring representation from all demographics is essential for developing a responsible and fair generative AI application, especially in sensitive domains like loan approval decisions. This approach helps reduce bias and supports ethical AI practices.

• Question 258:

  A company wants to automatically convert recorded customer calls into text so that analysts can review the conversations.

  Which AWS service should the company use?

  A. Amazon Translate
  B. Amazon Rekognition
  C. Amazon Transcribe
  D. Amazon Textract
  C. Amazon Transcribe

  ---

  Explanation

  Amazon Transcribe is the correct answer because it is designed to convert speech into text. This makes it suitable for processing recorded customer calls.

  Option C (Correct): "Amazon Transcribe": This is correct because it performs automatic speech recognition for audio content.

  Option A: "Amazon Translate" is incorrect because it translates text between languages.

  Option B: "Amazon Rekognition" is incorrect because it analyzes images and videos.

  Option D: "Amazon Textract" is incorrect because it extracts text from documents, not audio.

• Question 259:

  Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

  A. One-shot prompting
  B. Prompt chaining
  C. Tree of thoughts
  D. Retrieval Augmented Generation (RAG)
  B. Prompt chaining

  ---

  Prompt chaining involves breaking a complex task into smaller, manageable subtasks and passing them sequentially to a large language model. Each step builds upon the previous one, enabling more structured reasoning and improved task execution.

• Question 260:

  A company is using Amazon Bedrock to build an AI assistant. The AI assistant helps customers find relevant products by making suggestions. However, the AI assistant's responses are often generic and irrelevant. The company wants to use prompt engineering to improve the AI assistant's responses. Which solution will meet these requirements?

  A. Use few-shot prompting to add domain-specific context and explicit instructions.
  B. Use chain-of-thought prompting with hidden reasoning steps to ignore explicit domain instructions.
  C. Modify the AI assistant's conversational style to use more formal language and include technical product specifications.
  D. Use zero-shot prompting to augment retrieval from a product database.
  A. Use few-shot prompting to add domain-specific context and explicit instructions.

  ---

  Few-shot prompting provides the model with examples and domain-specific context, guiding it to generate more accurate and relevant responses. This improves the quality of product suggestions compared to generic outputs.