Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 431:

  Which AWS service creates business intelligence reports and automatically generates executive summaries based on data that users provide?

  A. Amazon Q in QuickSight
  B. Amazon Rekognition
  C. Amazon Textract
  D. Amazon Polly
  A. Amazon Q in QuickSight

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  Explanation

  Amazon Q in QuickSight provides generative BI capabilities, allowing users to create reports, analyze data, and automatically generate natural language summaries and insights from datasets.

• Question 432:

  A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.

  Which stage of the ML pipeline is the company currently in?

  A. Data pre-processing
  B. Feature engineering
  C. Exploratory data analysis
  D. Hyperparameter tuning
  C. Exploratory data analysis

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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 433:

  A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.

  The data is encrypted with Amazon S3 managed keys (SSE-S3). The FM encounters a failure when attempting to access the S3 bucket data.

  Which solution will meet these requirements?

  A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
  B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  C. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  D. Ensure that the S3 data does not contain sensitive information.
  A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.

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  Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE- S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.

  Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key":This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.

  Option B:"Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.

  Option C:"Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.

  Option D:"Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.

  References:

  Managing Access to Encrypted Data in AWS:AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.

• Question 434:

  A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.

  Which solution scope gives the company the MOST ownership of security responsibilities?

  A. Using a third-party enterprise application that has embedded generative AI features.
  B. Building an application by using an existing third-party generative AI foundation model (FM).
  C. Refining an existing third-party generative AI foundation model (FM) by fine-tuning the model by using data specific to the business.
  D. Building and training a generative AI model from scratch by using specific data that a customer owns.
  D. Building and training a generative AI model from scratch by using specific data that a customer owns.

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  Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the model, and the infrastructure.

  Option D (Correct): "Building and training a generative AI model from scratch by using specific data that a customer owns":This is the correct answer because it involves complete ownership of the model, data, and infrastructure, giving the company the highest level of responsibility for security.

  Option A:"Using a third-party enterprise application that has embedded generative AI features" is incorrect as the company has minimal control over the security of the AI features embedded within a third-party application.

  Option B:"Building an application using an existing third-party generative AI foundation model (FM)" is incorrect because security responsibilities are shared with the third- party model provider.

  Option C:"Refining an existing third-party generative AI FM by fine-tuning the model with business-specific data" is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.

  References:

  Generative AI Security Scoping Matrix on AWS:AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.

• Question 435:

  Which AWS feature records details about ML instance data for governance and reporting?

  A. Amazon SageMaker Model Cards
  B. Amazon SageMaker Debugger
  C. Amazon SageMaker Model Monitor
  D. Amazon SageMaker JumpStart
  A. Amazon SageMaker Model Cards

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  Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used

  for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.

  Amazon SageMaker Model Cards

• Question 436:

  HOTSPOT

  A company needs to customize a base model that is hosted on Amazon Bedrock.

  Select the correct model customization method from the following list of company requirements. Each model customization method should be selected one or more times.

  

  

  Explanation:

• Question 437:

  A company has set up a translation tool to help its customer service team handle issues from customers around the world. The company wants to evaluate the performance of the translation tool. The company sets up a parallel data process that compares the responses from the tool to responses from actual humans. Both sets of responses are generated on the same set of documents. Which strategy should the company use to evaluate the translation tool?

  A. Use the Bilingual Evaluation Understudy (BLEU) score to estimate the absolute translation quality of the two methods.
  B. Use the Bilingual Evaluation Understudy (BLEU) score to estimate the relative translation quality of the two methods.
  C. Use the BERTScore to estimate the absolute translation quality of the two methods.
  D. Use the BERTScore to estimate the relative translation quality of the two methods.
  B. Use the Bilingual Evaluation Understudy (BLEU) score to estimate the relative translation quality of the two methods.

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  BLEU measures n-gram overlap between a candidate translation and one or more reference translations. When you apply it to both the tool's outputs and the human outputs on the same source documents, you get comparable scores that let you directly assess which approach yields better translations.

• Question 438:

  Which prompting attack directly exposes the configured behavior of a large language model (LLM)?

  A. Prompted persona switches
  B. Exploiting friendliness and trust
  C. Ignoring the prompt template
  D. Extracting the prompt template
  D. Extracting the prompt template

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  "Extracting the prompt template" is a type of prompting attack that involves directly exposing the configured behavior or the underlying system prompt of a large language model (LLM). This attack can reveal sensitive details about how the model operates, including its internal instructions or restrictions, which are typically not intended to be disclosed to the user. Such an exposure can compromise the security and reliability of the LLM by making it vulnerable to further exploitation or misuse.

• Question 439:

  A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models. Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?

  A. Crowd-sourced evaluation
  B. Automatic model evaluation
  C. Model evaluation with human workers
  D. Reinforcement learning from human feedback (RLHF)
  B. Automatic model evaluation

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  Automatic model evaluation is the strategy that allows the company to evaluate the LLMs with the least operational overhead. This method leverages automated tools and processes to assess the toxicity or quality of the generated output without the need for manual intervention or crowd-sourced input. By using pre-built evaluation metrics or toxicity detection models, the company can quickly and efficiently evaluate multiple models without the complexity and time required for human evaluations.

• Question 440:

  Which statement accurately describes Retrieval Augmented Generation (RAG)?

  A. A process that uses large amounts of new data to train large language models (LLMs) to improve LLM performance
  B. A process by which large language models (LLMs) reference external authoritative knowledge bases to enhance the relevance and accuracy of LLM responses without re-training
  C. A process that limits large language models (LLMs) exclusively to their original training data to improve response speed for business applications without re-training
  D. A process that focuses on language translation tasks for businesses that operate in multiple countries
  B. A process by which large language models (LLMs) reference external authoritative knowledge bases to enhance the relevance and accuracy of LLM responses without re-training

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  Explanation

  Retrieval Augmented Generation enhances model outputs by retrieving relevant information from external authoritative data sources at inference time and using that information to generate more accurate and contextually relevant responses without retraining the model.