Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 411:

  A company is monitoring a predictive model by using Amazon SageMaker Model Monitor. The company notices data drift beyond a defined threshold. The company wants to mitigate a potentially adverse impact on the predictive model. Which solution will meet these requirements?

  A. Restart the SageMaker AI endpoint.
  B. Adjust the monitoring sensitivity.
  C. Re-train the model with fresh data.
  D. Set up experiments tracking.
  C. Re-train the model with fresh data.

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  When Amazon SageMaker Model Monitor detects data drift beyond a defined threshold, it indicates that the current data differs from what the model was trained on. Retraining the model with recent data ensures it adapts to new patterns and maintains predictive accuracy.

• Question 412:

  A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files. Which solution meets these requirements MOST cost-effectively?

  A. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  B. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  C. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine- tuned model to process user prompts.
  D. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
  D. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.

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  Using an Amazon Bedrock knowledge base allows the model to efficiently access relevant information from the PDF manuals when needed, reducing the cost compared to continuously fine-tuning a model or providing all PDFs as context in each prompt. This approach ensures that only necessary context is provided, making it cost-effective.

• Question 413:

  A company has created multiple ML models. The company needs a solution for storing, managing, and versioning the models. Which AWS service or feature meets these requirements?

  A. AWS Audit Manager
  B. Amazon SageMaker Model Monitor
  C. Amazon SageMaker Model Registry
  D. Amazon SageMaker Canvas
  C. Amazon SageMaker Model Registry

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  Amazon SageMaker Model Registry is the AWS service designed to store, manage, and version machine learning models. It provides a central repository for tracking model versions and managing their deployment status throughout the ML lifecycle.

• Question 414:

  A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals. Which model evaluation strategy meets these requirements?

  A. Bilingual Evaluation Understudy (BLEU)
  B. Root mean squared error (RMSE)
  C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  D. F1 score
  A. Bilingual Evaluation Understudy (BLEU)

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  BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.

  Option A (Correct): "Bilingual Evaluation Understudy (BLEU)":This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.

  Option B:"Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.

  Option C:"Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.

  Option D:"F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.

  References:

  Model Evaluation Metrics on AWS:AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

• Question 415:

  Which phase of the ML lifecycle determines compliance and regulatory requirements?

  A. Feature engineering
  B. Model training
  C. Data collection
  D. Business goal identification
  C. Data collection

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  The data collection phase of the ML lifecycle is the most relevant for determining compliance and regulatory requirements. During this phase, organizations must ensure that the data being collected and used for training the model complies with legal and regulatory standards, such as data privacy laws (e.g., GDPR, HIPAA), industry-specific regulations, and ethical considerations. The organization must also verify that they have the proper consent to use the data and that the data does not contain any biases or violate any regulations.

• Question 416:

  A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure. Which solution meets these requirements?

  A. Deploy the model on an Amazon EC2 instance.
  B. Deploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
  C. Deploy the model by using Amazon CloudFront with an Amazon S3 integration.
  D. Deploy the model by using an Amazon SageMaker endpoint.
  D. Deploy the model by using an Amazon SageMaker endpoint.

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  Amazon SageMaker provides a fully managed service for deploying machine learning models at scale without needing to manage servers or infrastructure. When you deploy a model using Amazon SageMaker endpoints, it handles all the infrastructure, scaling, and maintenance aspects. This allows the company to focus solely on making predictions and integrating the model into their application, without worrying about managing servers or clusters. Amazon SageMaker simplifies the deployment of ML models by providing scalable, secure, and serverless endpoints, which are ideal for serving predictions in real-time.

• Question 417:

  A company wants to add generative AI functionality to its application by integrating a large language model (LLM). The responses from the LLM must be as deterministic and as stable as possible. Which solution meets these requirements?

  A. Configure the application to automatically set the temperature parameter to 0 when submitting the prompt to the LLM.
  B. Configure the application to automatically add "make your response deterministic" at the end of the prompt before submitting the prompt to the LLM.
  C. Configure the application to automatically add "make your response deterministic" at the beginning of the prompt before submitting the prompt to the LLM.
  D. Configure the application to automatically set the temperature parameter to 1 when submitting the prompt to the LLM.
  A. Configure the application to automatically set the temperature parameter to 0 when submitting the prompt to the LLM.

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  The temperature hyperparameter directly controls the randomness of the LLM's token sampling: setting it to0 forces greedy decoding, yielding the most likely next token every time and thus fully deterministic, stable outputs.

• Question 418:

  A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.

  Which solution will meet this requirement?

  A. Use a different FM
  B. Choose a lower temperature value
  C. Create an Amazon Bedrock knowledge base
  D. Enable model invocation logging
  C. Create an Amazon Bedrock knowledge base

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  Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.

  Option C (Correct): "Create an Amazon Bedrock knowledge base":This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.

  Option A:"Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.

  Option B:"Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.

  Option D:"Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.

  References:

  Amazon Bedrock and Knowledge Integration:AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.

• Question 419:

  A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.

  The company has a labeled dataset that contains user messages and output JSON files.

  Which solution will train the LLM for workflow automation?

  A. Unsupervised learning
  B. Continued pre-training
  C. Fine-tuning
  D. Reinforcement learning from human feedback (RLHF)
  C. Fine-tuning

• Question 420:

  A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly. What should the company do to mitigate this problem?

  A. Reduce the volume of data that is used in training.
  B. Add hyperparameters to the model.
  C. Increase the volume of data that is used in training.
  D. Increase the model training time.
  C. Increase the volume of data that is used in training.

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  When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.

  Option C (Correct): "Increase the volume of data that is used in training":Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.

  Option A:"Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.

  Option B:"Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.

  Option D:"Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.

  References:

  Best Practices for Model Training on AWS:AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.