Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 1:

  HOTSPOT

  A company wants more customized responses to its generative AI models' prompts.

  Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)

  1. Continued pre-training

  2. Data augmentation

  3. Model fine-tuning

  

  

  Explanation:

  The models must be taught a new domain-specific task - Model fine-tuning is the best approach when a model needs to learn a specific domain-related task. It involves Fine-tuning adjusting a pre-trained model with a smaller, task-specific dataset to improve performance in that domain.

  A limited amount of labeled data is available and more data is needed - Data augmentation helps in scenarios where labeled data is scarce by artificially increasing the size and Data augmentation variability of the dataset, improving model generalization.

  Only unlabeled data is available - Continued pre-training allows an existing foundation model to be further trained on domain-specific, Continued pre-training unlabeled data to adapt to new contexts without requiring labeled data.

• Question 2:

  A company is analyzing financial transaction records. The company categorizes the records as either personal or business. The company inserts the categories into the transaction records. Which data preparation step does this describe?

  A. Data encoding
  B. Data labeling
  C. Data normalization
  D. Data balancing
  B. Data labeling

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  Assigning categories such as "personal" or "business" to financial transaction records is an example of data labeling, where each data point is tagged with a specific label for use in machine learning or data analysis tasks.

• Question 3:

  What is an example of structured data?

  A. A file of text comments from an online forum
  B. A compilation of video files that contains news broadcasts
  C. A CSV file that consists of measurement data
  D. Transcribed conversations between call center agents and customers
  C. A CSV file that consists of measurement data

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  Explanation

  A CSV file that contains measurement data is structured because the information is organized into a consistent tabular format with defined rows and columns.

• Question 4:

  A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment. Which Amazon Bedrock pricing model meets these requirements?

  A. On-Demand
  B. Model customization
  C. Provisioned Throughput
  D. Spot Instance
  A. On-Demand

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  Amazon Bedrock offers an on-demand pricing model that provides flexibility without long- term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.

  Option A (Correct): "On-Demand":This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.

  Option B:"Model customization" is a feature, not a pricing model.

  Option C:"Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.

  Option D:"Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.

  References:

  AWS Pricing Models for Flexibility:On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost- effectiveness for projects with variable usage patterns.

• Question 5:

  A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy. Which solution meets these requirements?

  A. Use Amazon SageMaker and iterate with newer data.
  B. Use Amazon Personalize and iterate with historical data.
  C. Use Amazon CloudWatch to analyze customer orders.
  D. Use Amazon Rekognition to optimize the model.
  A. Use Amazon SageMaker and iterate with newer data.

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  To continuously improve the accuracy of the machine learning model, Amazon SageMaker is the appropriate solution. SageMaker allows for efficient model training, deployment, and iteration. The company can use SageMaker to retrain the model regularly with newer data to account for changes in customer behavior, market trends, and other dynamic factors that may affect food waste and sales revenue. Iterating with newer data helps improve the model's performance over time, ensuring it remains accurate and relevant.

• Question 6:

  A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data. Which solution meets these requirements?

  A. Use Amazon Bedrock Guardrails.
  B. Remove personally identifiable information (PII) from the customer data before fine- tuning the LLM.
  C. Increase the Top-K parameter of the LLM.
  D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.
  B. Remove personally identifiable information (PII) from the customer data before fine- tuning the LLM.

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  Preprocessing your fine-tuning dataset to strip out names, account numbers, and other PII prevents the model from ever ingesting - or later regurgitating - private customer information. This data sanitization step is the most reliable way to guarantee that sensitive details aren't embedded into the model.

• Question 7:

  A global financial company has developed an ML application to analyze stock market data and provide stock market trends. The company wants to continuously monitor the application development phases and to ensure that company policies and industry regulations are followed. Which AWS services will help the company assess compliance requirements? (Choose two.)

  A. AWS Audit Manager
  B. AWS Config
  C. Amazon Inspector
  D. Amazon CloudWatch
  E. AWS CloudTrail
  A. AWS Audit Manager
  B. AWS Config

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  AWS Audit Manager helps organizations continuously assess and audit compliance with industry regulations and internal policies by automating evidence collection and generating audit reports. This is essential for ensuring that the ML application meets regulatory requirements. AWS Config enables continuous monitoring and compliance checks by tracking configuration changes in AWS resources. It helps the company ensure that infrastructure settings align with security policies and industry standards.

• Question 8:

  An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to build a mechanism that the ML team can use to audit models. Which solution should the ML team use when publishing the custom ML models?

  A. Create documents with the relevant information. Store the documents in Amazon S3.
  B. Use AWS AI Service Cards for transparency and understanding models.
  C. Create Amazon SageMaker Model Cards with intended uses and training and inference details.
  D. Create model training scripts. Commit the model training scripts to a Git repository.
  C. Create Amazon SageMaker Model Cards with intended uses and training and inference details.

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  Amazon SageMaker Model Cards are designed to document the essential details about machine learning models, including their intended uses, training datasets, training parameters, evaluation metrics, and inference environment. This provides a centralized mechanism to store and audit the metadata of the models, which is ideal for the ML team's need to share and audit models effectively. Key benefits of Amazon SageMaker Model Cards:

  1.

  Standardized documentation of models' characteristics and intended use cases.

  2.

  Transparency and traceability for auditing purposes.

  3.

  Integration with other AWS services for lifecycle management.

• Question 9:

  Which functionality does Amazon SageMaker Clarify provide?

  A. Integrates a Retrieval Augmented Generation (RAG) workflow
  B. Monitors the quality of ML models in production
  C. Documents critical details about ML models
  D. Identifies potential bias during data preparation
  D. Identifies potential bias during data preparation

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  Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.

  Option C (Correct): "Exploratory data analysis":This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.

  Option A:"Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.

  Option B:"Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.

  Option D:"Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.

  References:

  Stages of the Machine Learning Pipeline:AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.

• Question 10:

  A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations. Which solution will meet these requirements?

  A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
  B. Data augmentation by using an Amazon Bedrock knowledge base
  C. Image recognition by using Amazon Rekognition
  D. Data summarization by using Amazon QuickSight
  A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus

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  Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to validate the annotations and reduce the risk of incorrect annotations.