Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 291:

  A company wants to identify potential bias in an ML model used for hiring recommendations. The model's predictions depend on demographic information. Which AWS service should the company use?

  A. Amazon SageMaker Clarify
  B. Amazon SageMaker Ground Truth
  C. Amazon GuardDuty
  D. Amazon Macie
  A. Amazon SageMaker Clarify

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  SageMaker Clarify detects bias in datasets and model predictions and provides explainability reports. This enables organizations to ensure fairness and compliance for sensitive applications such as hiring.

• Question 292:

  A company wants to monitor a generative AI application and receive an alert when invocation latency becomes higher than an acceptable threshold.

  Which AWS service should the company use?

  A. Amazon CloudWatch
  B. Amazon Textract
  C. Amazon Comprehend
  D. AWS Artifact
  A. Amazon CloudWatch

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  Explanation

  Amazon CloudWatch is the correct answer because it can monitor metrics and generate alarms when operational thresholds, such as invocation latency, are exceeded.

  Option A (Correct): "Amazon CloudWatch": This is correct because it supports monitoring and alerting for application metrics.

  Option B: "Amazon Textract" is incorrect because it extracts data from documents.

  Option C: "Amazon Comprehend" is incorrect because it performs text analysis.

  Option D: "AWS Artifact" is incorrect because it provides compliance documents.

• Question 293:

  A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products. Which methodology should the company use to meet these requirements?

  A. Supervised learning
  B. Unsupervised learning
  C. Reinforcement learning
  D. Reinforcement learning from human feedback (RLHF)
  B. Unsupervised learning

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  Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs.

• Question 294:

  How can companies use large language models (LLMs) securely on Amazon Bedrock?

  A. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
  B. Enable AWS Audit Manager for automatic model evaluation jobs.
  C. Enable Amazon Bedrock automatic model evaluation jobs.
  D. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
  A. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.

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  To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.

  Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access":This is the correct answer as it directly addresses both security practices in prompt design and access management.

  Option B:"Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.

  Option C:"Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.

  Option D:"Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.

  References:

  Secure AI Practices on AWS:AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.

• Question 295:

  A manufacturing company has an application that ingests consumer complaints from publicly available sources. The application uses complex hard-coded logic to process the complaints. The company wants to scale this logic across markets and product lines. Which advantage do generative AI models offer for this scenario?

  A. Predictability of outputs
  B. Adaptability
  C. Less sensitivity to changes in inputs
  D. Explainability
  B. Adaptability

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  Generative AI models can generalize learned patterns to new domains and formats with minimal manual reconfiguration. This adaptability lets you extend complaint-processing logic across different markets and product lines without rewriting complex hard-coded rules.

• Question 296:

  An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms. What should the firm do when developing and deploying the LLM? (Select TWO.)

  A. Include fairness metrics for model evaluation.
  B. Adjust the temperature parameter of the model.
  C. Modify the training data to mitigate bias.
  D. Avoid overfitting on the training data.
  E. Apply prompt engineering techniques.
  A. Include fairness metrics for model evaluation.
  C. Modify the training data to mitigate bias.

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  To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment. A. Include Fairness Metrics for Model Evaluation:

• Question 297:

  A company wants to use Amazon Bedrock. The company needs to review which security aspects the company is responsible for when using Amazon Bedrock. Which security aspect will the company be responsible for?

  A. Patching and updating the versions of Amazon Bedrock
  B. Protecting the infrastructure that hosts Amazon Bedrock
  C. Securing the company's data in transit and at rest
  D. Provisioning Amazon Bedrock within the company network
  C. Securing the company's data in transit and at rest

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  According to the AWS Shared Responsibility Model, AWS manages the security of the cloud, including the infrastructure and services like Amazon Bedrock. Customers are responsible for security in the cloud, which encompasses protecting their data, managing access controls, and configuring security settings for their applications. Reference: https://aws.amazon.com/compliance/shared-responsibility-model/?nc1=h_ls

• Question 298:

  A security company is using Amazon Bedrock to run foundation models (FMs). The company wants to ensure that only authorized users invoke the models. The company needs to identify any unauthorized access attempts to set appropriate AWS Identity and Access Management (IAM) policies and roles for future iterations of the FMs. Which AWS service should the company use to identify unauthorized users that are trying to access Amazon Bedrock?

  A. AWS Audit Manager
  B. AWS CloudTrail
  C. Amazon Fraud Detector
  D. AWS Trusted Advisor
  B. AWS CloudTrail

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  AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.

• Question 299:

  A company notices that its text generation model sometimes produces statements that sound convincing but are false.

  Which limitation of generative AI does this behavior represent?

  A. Overfitting
  B. Hallucination
  C. Clustering
  D. Tokenization
  B. Hallucination

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  Explanation

  Hallucination is the correct answer because it describes model outputs that appear plausible but are inaccurate or fabricated. This is a known limitation of generative AI systems.

  Option B (Correct): "Hallucination": This is correct because it directly refers to generated content that is not grounded in fact.

  Option A: "Overfitting" is incorrect because overfitting refers to poor generalization from training data.

  Option C: "Clustering" is incorrect because clustering is an unsupervised learning technique.

  Option D: "Tokenization" is incorrect because tokenization is the process of splitting text into smaller units.

• Question 300:

  A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations?

  A. Adjust the prompt.
  B. Choose an LLM of a different size.
  C. Increase the temperature.
  D. Increase the Top K value.
  A. Adjust the prompt.

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  Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.