Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 391:

  HOTSPOT

  A company wants to create an application to summarize meetings by using meeting audio recordings.

  Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)

  1. Convert meeting audio recordings to meeting text files by using Amazon Polly.

  2. Convert meeting audio recordings to meeting text files by using Amazon Transcribe.

  3. Store meeting audio recordings in an Amazon S3 bucket.

  4. Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.

  5. Summarize meeting text files by using Amazon Bedrock.

  6. Summarize meeting text files by using Amazon Lex.

  

  

  Explanation:

• Question 392:

  Which scenario represents a practical use case for generative AI?

  A. Using an ML model to forecast product demand
  B. Employing a chatbot to provide human-like responses to customer queries in real time
  C. Using an analytics dashboard to track website traffic and user behavior
  D. Implementing a rule-based recommendation engine to suggest products to customers
  B. Employing a chatbot to provide human-like responses to customer queries in real time

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  A generative AI use case involves generating new content, such as natural language responses. A chatbot that provides human-like responses to customer queries uses generative AI models (like large language models) to dynamically create relevant answers, making this a practical example.

• Question 393:

  An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images. Which type of FM should the AI practitioner use to power the search application?

  A. Multi-modal embedding model
  B. Text embedding model
  C. Multi-modal generation model
  D. Image generation model
  A. Multi-modal embedding model

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  A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images.

• Question 394:

  A company wants to identify potential bias in training data before a model is deployed.

  Which AWS service or feature should the company use?

  A. Amazon SageMaker Clarify
  B. Amazon S3 Glacier
  C. Amazon EC2
  D. AWS Cost Explorer
  A. Amazon SageMaker Clarify

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  Explanation

  Amazon SageMaker Clarify is the correct answer because it helps detect potential bias in datasets and models and supports responsible AI practices.

  Option A (Correct): "Amazon SageMaker Clarify": This is correct because it is designed to analyze bias and explainability considerations.

  Option B: "Amazon S3 Glacier" is incorrect because it is long-term storage.

  Option C: "Amazon EC2" is incorrect because it provides compute capacity but not bias detection features.

  Option D: "AWS Cost Explorer" is incorrect because it is used for analyzing AWS costs.

• Question 395:

  Which type of ML technique provides the MOST explainability?

  A. Linear regression
  B. Support vector machines
  C. Random cut forest (RCF)
  D. Neural network
  A. Linear regression

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  Explanation

  Linear regression provides the highest explainability because its parameters directly show how each input feature influences the output, making the model's behavior transparent and easily interpretable.

• Question 396:

  An AI practitioner is using Amazon Bedrock Prompt Management to create a reusable prompt. The prompt must be able to interact with external services by calling an external API. Which solution will meet this requirement?

  A. Use special tokens.
  B. Use a tools configuration.
  C. Use prompt variables.
  D. Use a stop sequence.
  B. Use a tools configuration.

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  In Amazon Bedrock Prompt Management, a tools configuration allows prompts to call external APIs and services. This enables the AI assistant to extend functionality beyond static responses and interact with external systems.

• Question 397:

  A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock?

  A. Purchase Provisioned Throughput for the custom model.
  B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
  C. Register the model with the Amazon SageMaker Model Registry.
  D. Grant access to the custom model in Amazon Bedrock.
  A. Purchase Provisioned Throughput for the custom model.

• Question 398:

  A global company wants employees to query internal systems using natural language. The solution must support API calls, decision-making steps, and multi-step tasks. Which Amazon Bedrock feature meets these requirements?

  A. Knowledge Bases
  B. Prompt management
  C. Agents
  D. Model evaluation
  C. Agents

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  Amazon Bedrock Agents orchestrate multi-step tasks, call external APIs, and use reasoning to determine actions. They enable enterprise-grade AI assistants that perform business workflows, not just text generation.

• Question 399:

  A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm. Which type of data will meet this requirement?

  A. Text data
  B. Image data
  C. Time series data
  D. Binary data
  C. Time series data

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  The Amazon SageMaker DeepAR forecasting algorithm is specifically designed for forecasting scalar (one-dimensional) time series data using recurrent neural networks (RNNs). It excels when trained on datasets containing hundreds of related time series, enabling it to learn patterns across multiple series and provide accurate forecasts.

• Question 400:

  A company uses a third-party model on Amazon Bedrock to analyze confidential documents. The company is concerned about data privacy. Which statement describes how Amazon Bedrock protects data privacy?

  A. User inputs and model outputs are anonymized and shared with third-party model providers.
  B. User inputs and model outputs are not shared with any third-party model providers.
  C. User inputs are kept confidential, but model outputs are shared with third-party model providers.
  D. User inputs and model outputs are redacted before the inputs and outputs are shared with third-party model providers.
  B. User inputs and model outputs are not shared with any third-party model providers.

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  Amazon Bedrock processes your data entirely within AWS's secure environment and does not forward your inputs or the generated outputs back to the model vendor, ensuring that your confidential documents remain private.