Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 111:

  A company is using Amazon Q Business to create an AI assistant. The company needs to restrict user interactions with the AI assistant to company-approved topics.

  Which feature will meet these requirements?

  A. Amazon Q Business Enterprise index
  B. Amazon Q Business Starter index
  C. Amazon Q Business application guardrails
  D. Amazon Q index cross-account access
  C. Amazon Q Business application guardrails

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  Explanation

  Application guardrails in Amazon Q Business allow organizations to control and restrict the topics and types of interactions the AI assistant can respond to, ensuring conversations remain within company-approved boundaries.

• Question 112:

  An AI practitioner is developing a prompt for an Amazon Titan model. The model is hosted on Amazon Bedrock. The AI practitioner is using the model to solve numerical reasoning challenges. The AI practitioner adds the following phrase to the end of the prompt: "Ask the model to show its work by explaining its reasoning step by step." Which prompt engineering technique is the AI practitioner using?

  A. Chain-of-thought prompting
  B. Prompt injection
  C. Few-shot prompting
  D. Prompt templating
  A. Chain-of-thought prompting

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  Chain-of-thought prompting is a technique in prompt engineering where the AI model is encouraged to break down its reasoning process step by step to solve a problem, such as numerical reasoning challenges. By explicitly instructing the model to "show its work by explaining its reasoning step by step," the practitioner ensures the model provides a logical sequence of intermediate steps leading to the solution. This improves the accuracy and transparency of the model's outputs, particularly for complex reasoning tasks.

• Question 113:

  A customer service team is developing an application to analyze customer feedback and automatically classify the feedback into different categories. The categories include product quality, customer service, and delivery experience. Which AI concept does this scenario present?

  A. Computer vision
  B. Natural language processing (NLP)
  C. Recommendation systems
  D. Fraud detection
  B. Natural language processing (NLP)

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  Automatically analyzing and classifying free-text feedback into thematic categories is a core NLP task (text classification).

• Question 114:

  A company is deploying a new AI application to generate content for internal users.

  Which strategy will make the application output more deterministic?

  A. Decreasing the temperature
  B. Increasing the learning rate
  C. Setting stop sequences
  D. Setting the token count
  A. Decreasing the temperature

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  Explanation

  Decreasing the temperature reduces randomness in token selection, which makes the model's responses more consistent and deterministic for the same input.

• Question 115:

  A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?

  A. The temperature is set too high.
  B. The selected model does not support fine-tuning.
  C. The Top P value is too high.
  D. The input tokens exceed the model's context size.
  D. The input tokens exceed the model's context size.

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  Language models have a maximum context size, which limits the number of input tokens they can process at once. If a book's length exceeds this limit, the model cannot handle the full input, leading to failure in summarization.

• Question 116:

  Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?

  A. Providing a visually appealing summary of a model's capabilities.
  B. Standardizing information about a model's purpose, performance, and limitations.
  C. Reducing the overall computational requirements of a model.
  D. Physically storing models for archival purposes.
  B. Standardizing information about a model's purpose, performance, and limitations.

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  Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements.

  References:

  AWS SageMaker Model Cards Documentation.

• Question 117:

  What does an F1 score measure in the context of foundation model (FM) performance?

  A. Model precision and recall.
  B. Model speed in generating responses.
  C. Financial cost of operating the model.
  D. Energy efficiency of the model's computations.
  A. Model precision and recall.

• Question 118:

  A company is building a job recommendation system based on job posting data and job seeker user profiles. The system shows bias in job recommendations based on gender for user profiles that are otherwise equivalent.

  Which principle should the company follow to address this issue, according to AWS best practices for responsible AI?

  A. Governance
  B. Explainability
  C. Controllability
  D. Fairness
  D. Fairness

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  Explanation

  Fairness focuses on identifying and mitigating bias so that AI systems provide equitable outcomes for users with similar characteristics, preventing discriminatory behavior such as gender-based bias in job recommendations.

• Question 119:

  A company wants to develop an AI assistant for employees to query internal data. Which AWS service will meet this requirement?

  A. Amazon Rekognition
  B. Amazon Textract
  C. Amazon Lex
  D. Amazon Q Business
  D. Amazon Q Business

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  Amazon Q Business is designed to build generative AI assistants for querying and interacting with internal organizational data, making it the ideal service for creating an AI assistant for employees to access company information.

• Question 120:

  A company is training ML models on datasets. The datasets contain some classes that have more examples than other classes. The company wants to measure how well the model balances detecting and labeling the classes. Which metric should the company use?

  A. Accuracy
  B. Recall
  C. Precision
  D. F1 score
  D. F1 score

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  The F1 score is the harmonic mean of precision and recall, making it especially useful for evaluating model performance on datasets with class imbalance. It measures how well the model balances detecting (recall) and correctly labeling (precision) all classes.