Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 281:

  An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data. Which strategy should the AI practitioner use?

  A. Configure AWS CloudTrail as the logs destination for the model.
  B. Enable invocation logging in Amazon Bedrock.
  C. Configure AWS Audit Manager as the logs destination for the model.
  D. Configure model invocation logging in Amazon EventBridge.
  B. Enable invocation logging in Amazon Bedrock.

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  Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.

  Option B (Correct): "Enable invocation logging in Amazon Bedrock":This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.

  Option A:"Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.

  Option C:"Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.

  Option D:"Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.

  References:

  Amazon Bedrock Logging Capabilities:AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

• Question 282:

  A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.

  Which AWS service or feature meets these requirements?

  A. Amazon SageMaker JumpStart
  B. Amazon SageMaker HyperPod
  C. Amazon SageMaker Data Wrangler
  D. Amazon SageMaker Model Monitor
  D. Amazon SageMaker Model Monitor

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  Explanation

  SageMaker Model Monitor continuously measures data and prediction quality in production, detects deviations from your model's baseline (such as data drift or accuracy degradation), and alerts you so you can investigate and resolve issues promptly.

• Question 283:

  A company is building a large language model (LLM)-based AI assistant to support service agents by automatically managing customer inquiries. The company wants to reduce the effort that customer service agents require during support calls.

  The company needs to select a metric to evaluate the AI assistant against one of the company's business objectives.

  Which metric will meet these requirements?

  A. Website engagement rate
  B. Average call duration
  C. Agent attrition rate
  D. First contact resolution rate
  B. Average call duration

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  Explanation

  Average call duration directly measures how much effort service agents need during support calls. If the AI assistant is effectively helping manage inquiries, agents should be able to resolve calls more quickly, reducing call time.

• Question 284:

  A company wants to extract key insights from large policy documents to increase employee efficiency.

  Which generative AI strategy meets this requirement?

  A. Regression
  B. Clustering
  C. Summarization
  D. Classification
  C. Summarization

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  Summarization is a generative AI strategy that extracts and condenses the key insights from large documents, such as policy documents, making it easier for employees to quickly understand and act on essential information.

• Question 285:

  A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers. Which solution will meet these requirements?

  A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
  B. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
  C. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
  D. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.
  A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.

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  To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.

• Question 286:

  HOTSPOT

  A company is using Amazon SageMaker to develop AI models.

  Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.)

  

  

  Explanation:

• Question 287:

  A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company's products. Which prompt engineering technique meets these requirements?

  A. Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.
  B. Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.
  C. Include a diverse range of product features in each prompt to generate creative and unique descriptions.
  D. Provide detailed, product-specific prompts to ensure precise and customized descriptions.
  B. Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.

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  To generate concise, feature-specific descriptions for each product, the company should create prompts tailored to specific product categories. By highlighting the key features of each product category in the prompt, the model can focus on generating descriptions that are relevant and aligned with the unique attributes of each product. Additionally, specifying the desired output format and length ensures that the responses meet the company's requirements for conciseness and clarity.

  Tailored prompts help ensure the model generates relevant and accurate descriptions by focusing on the most important features of each product category.

  Desired output format and length ensure the responses are consistent and concise, as required.

• Question 288:

  A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.

  Which Amazon SageMaker inference option will meet these requirements?

  A. Batch transform
  B. Real-time inference
  C. Serverless inference
  D. Asynchronous inference
  A. Batch transform

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  Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in size. This method processes data in batches, making it suitable for analyzing archived data without the need for real- time access to predictions.

  Option A (Correct): "Batch transform":This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.

  Option B:"Real-time inference" is incorrect because it is used for low-latency, real- time prediction needs, which is not required in this case.

  Option C:"Serverless inference" is incorrect because it is designed for small-scale, intermittent inference requests, not for large batch processing.

  Option D:"Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large datasets.

  References:

  Batch Transform on AWS SageMaker:AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost- effectiveness and scalability.

• Question 289:

  A company has created a custom model by fine-tuning an existing large language model (LLM) from Amazon Bedrock. The company wants to deploy the model to production and use the model to handle a steady rate of requests each minute. Which solution meets these requirements MOST cost-effectively?

  A. Deploy the model by using an Amazon EC2 compute optimized instance.
  B. Use the model with on-demand throughput on Amazon Bedrock.
  C. Store the model in Amazon S3 and host the model by using AWS Lambda.
  D. Purchase Provisioned Throughput for the model on Amazon Bedrock.
  D. Purchase Provisioned Throughput for the model on Amazon Bedrock.

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  Provisioned Throughput is priced lower per request when you have a predictable, steady volume of calls. By committing to a fixed throughput level, you secure the necessary capacity at a reduced unit cost compared to on-demand, making it the most cost-effective choice for steady-minute usage.

• Question 290:

  A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks. Which ML strategy meets these requirements?

  A. Increase the number of epochs.
  B. Use transfer learning.
  C. Decrease the number of epochs.
  D. Use unsupervised learning.
  B. Use transfer learning.

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  Transfer learning allows the company to adapt pre-trained models for new, related tasks, saving time and resources compared to training models from scratch. The other options do not address the goal of reusing existing models.