Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 11:

  An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.

  Which problem is the LLM having?

  A. Data leakage
  B. Hallucination
  C. Overfitting
  D. Underfitting
  B. Hallucination

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  In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problem often occurs with large language models (LLMs) when they create information that sounds correct but is actually incorrect or fabricated.

  Option B (Correct): "Hallucination":This is the correct answer because the problem described involves generating content that sounds factual but is incorrect, which is characteristic of hallucination in generative AI models.

  Option A:"Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating incorrect content.

  Option C:"Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.

  Option D:"Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.

  References:

  Large Language Models on AWS:AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.

• Question 12:

  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

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  AWS CloudTrail is the AWS service designed for logging and monitoring API calls made to AWS services, including Amazon Bedrock. CloudTrail records detailed information about the API requests, including the identity of the requester, the time of the request, and the source IP address. This service is ideal for logging all requests made to the Amazon Bedrock API and meets the logging requirement.

  Amazon S3 Intelligent-Tiering is a storage class designed for storing data that has unpredictable access patterns. It automatically moves data between two access tiers (frequent and infrequent) based on usage, which helps reduce costs while ensuring data is still available when needed. For retaining logs securely over 5 years at the lowest possible cost, this storage class provides an efficient way to handle long-term storage requirements without incurring unnecessary costs.

• Question 13:

  A company is creating a model to label credit card transactions. The company has a large volume of sample transaction data to train the model. Most of the transaction data is unlabeled. The data does not contain confidential information.

  The company needs to obtain labeled sample data to fine-tune the model.

  Which solutions will meet these requirements? (Choose two.)

  A. Run batch inference jobs on the unlabeled data.
  B. Run an Amazon SageMaker AI training job that uses the PyTorch Distributed library to label data.
  C. Use an Amazon SageMaker Ground Truth labeling job with Amazon Mechanical Turk workers.
  D. Use an optical character recognition model trained on labeled samples to label unlabeled samples.
  E. Run an Amazon SageMaker AI labeling job.
  C. Use an Amazon SageMaker Ground Truth labeling job with Amazon Mechanical Turk workers.
  E. Run an Amazon SageMaker AI labeling job.

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  Explanation

  Amazon SageMaker Ground Truth with Mechanical Turk workers enables human labeling of large unlabeled datasets, and SageMaker AI labeling jobs can automatically generate labels using machine learning workflows, providing the labeled data needed for fine-tuning.

• Question 14:

  Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?

  A. Expanding initiatives across business units to create long-term business value
  B. Ensuring alignment with business standards, revenue goals, and stakeholder expectations
  C. Overcoming challenges to drive business transformation and growth
  D. Developing policies and guidelines for data, transparency, responsible AI, and compliance
  D. Developing policies and guidelines for data, transparency, responsible AI, and compliance

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  AI governance frameworks focus on building trust and ensuring the responsible deployment of AI technologies. They establish clear policies and guidelines to address critical aspects such as data management, transparency, ethical considerations, responsible AI practices, and regulatory compliance. These frameworks help organizations mitigate risks, promote fairness, and foster public trust in AI systems, making them essential for creating human-centered AI technologies.

• Question 15:

  A company wants to reduce the risk that a generative AI application will return toxic or restricted content to users.

  Which Amazon Bedrock capability should the company use?

  A. Guardrails for Amazon Bedrock
  B. Amazon EC2 Auto Scaling
  C. AWS Cost Explorer
  D. Amazon S3 Glacier
  A. Guardrails for Amazon Bedrock

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  Explanation

  Guardrails for Amazon Bedrock is the correct answer because it helps apply safety controls and content filtering to model inputs and outputs. This supports safer generative AI applications.

  Option A (Correct): "Guardrails for Amazon Bedrock": This is correct because it helps enforce content policies and safety controls.

  Option B: "Amazon EC2 Auto Scaling" is incorrect because it manages compute scaling, not response safety.

  Option C: "AWS Cost Explorer" is incorrect because it is used for cost analysis.

  Option D: "Amazon S3 Glacier" is incorrect because it is archival storage.

• Question 16:

  HOTSPOT

  A company is building an AI assistant application. The company must implement a core governance process for the application development project. The company must ensure that the application aligns with responsible AI practices.

  Select and order the steps from the following list to correctly describe the implementation of a core governance process for this use case. Select each step one time.

  

  

• Question 17:

  Which term refers to the instructions given to foundation models (FMs) so that the FMs provide a more accurate response to a question?

  A. Prompt
  B. Direction
  C. Dialog
  D. Translation
  A. Prompt

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  A prompt is the input instruction or query you provide to a foundation model to guide its response, ensuring the model understands the task and delivers a more accurate answer.

• Question 18:

  A company is building a generative AI application to help customers make travel reservations. The application will process customer requests and invoke the appropriate API calls to complete reservation transactions.

  Which Amazon Bedrock resource will meet these requirements?

  A. Agents
  B. Intelligent prompt routing
  C. Knowledge Bases
  D. Guardrails
  A. Agents

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  Explanation

  Agents in Amazon Bedrock are designed to interpret user requests, orchestrate workflows, and automatically invoke the appropriate APIs and services to complete tasks such as reservation transactions in a generative AI application.

• Question 19:

  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
  B. Amazon Bedrock

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  Explanation

  Amazon Bedrock provides a managed platform that offers access to multiple foundation models from different providers, enabling organizations to evaluate, select, and use the most suitable models for specific generative AI use cases.

• Question 20:

  A company wants to control employee access to publicly available foundation models (FMs). Which solution meets these requirements?

  A. Analyze cost and usage reports in AWS Cost Explorer.
  B. Download AWS security and compliance documents from AWS Artifact.
  C. Configure Amazon SageMaker JumpStart to restrict discoverable FMs.
  D. Build a hybrid search solution by using Amazon OpenSearch Service.
  C. Configure Amazon SageMaker JumpStart to restrict discoverable FMs.

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  SageMaker JumpStart lets you curate which publicly available foundation models appear in your account's model catalog. By using JumpStart's model catalog settings (in combination with IAM), you can prevent employees from discovering or deploying any FMs you haven't explicitly allowed.