Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 311:

  A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development. Which solution meets these requirements?

  A. Track the model changes by using Git.
  B. Track the model changes by using Amazon Fraud Detector.
  C. Track the model changes by using Amazon SageMaker Model Cards.
  D. Track the model changes by using Amazon Comprehend.
  C. Track the model changes by using Amazon SageMaker Model Cards.

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  SageMaker Model Cards provide a built-in framework for capturing and versioning key metadata, such as training data details, performance metrics, lineage, and intended use, for each model iteration. This standardized documentation and history of development fulfills requirements for model version tracking and auditable records.

• Question 312:

  A company wants to create an ML model to predict customer satisfaction. The company needs fully automated model tuning. Which AWS service meets these requirements?

  A. Amazon Personalize
  B. Amazon SageMaker
  C. Amazon Athena
  D. Amazon Comprehend
  B. Amazon SageMaker

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  Amazon SageMaker provides a fully managed environment for building, training, and deploying machine learning models, including automatic model tuning. Specifically, SageMaker includes a feature called Automatic Model Tuning (or Hyperparameter Optimization), which automates the process of finding the best hyperparameters for your machine learning model. This is essential when you want to optimize the model's performance without manual intervention. Amazon SageMaker allows you to automate the training process and hyperparameter tuning, which aligns perfectly with the company's need for fully automated model tuning.

• Question 313:

  A company is using large language models (LLMs) to develop online tutoring applications. The company needs to apply configurable safeguards to the LLMs. These safeguards must ensure that the LLMs follow standard safety rules when creating applications. Which solution will meet these requirements with the LEAST effort?

  A. Amazon Bedrock playgrounds
  B. Amazon SageMaker Clarify
  C. Amazon Bedrock Guardrails
  D. Amazon SageMaker Jumpstart
  C. Amazon Bedrock Guardrails

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  Amazon Bedrock Guardrails provide configurable safeguards that enforce standard safety rules on large language models (LLMs) with minimal effort, making it easy to ensure safe and compliant AI application development.

• Question 314:

  An AI practitioner is evaluating the performance of an Amazon SageMaker model. The AI practitioner must choose a performance metric. The metric must show the ratio of the number of correctly classified items to the total number of correctly and incorrectly classified items. Which metric meets these requirements?

  A. Accuracy
  B. Precision
  C. F1 score
  D. Recall
  A. Accuracy

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  Accuracy is defined as the number of correctly classified items (both true positives and true negatives) divided by the total number of items evaluated (the sum of true positives, true negatives, false positives, and false negatives), matching exactly the ratio described.

• Question 315:

  An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company's website 24 hours a day, 7 days a week. Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?

  A. Data leakage
  B. Prompt injection
  C. Large language model (LLM) hallucinations
  D. Concept drift
  B. Prompt injection

• Question 316:

  HOTSPOT

  Select the correct prompt engineering technique from the following list for each description. Select each prompt engineering technique one time or not at all.

  

  

  Explanation:

  Provide a small number of examples to the model to understand the desired task before generating outputs -> Few-shot prompting Prompt a model to break down the step-by-step process that the model took to arrive at a final answer -> Chain-of-thought prompting Prompt a model to perform a task without providing examples -> Zero-shot prompting

  Few-shot prompting uses a few examples to guide the model's outputs. Chain-of-thought prompting asks the model to explain its reasoning step-by-step. Zero-shot prompting asks the model to perform a task without any examples.

• Question 317:

  A company has deployed an AI application in production on AWS. The application's responses have become less accurate over time.

  The company needs a solution to send alerts when the application performance drifts.

  Which AWS service or feature will meet this requirement?

  A. Amazon Augmented AI (Amazon A2I)
  B. Amazon SageMaker Model Monitor
  C. Amazon Rekognition
  D. AWS Trusted Advisor
  B. Amazon SageMaker Model Monitor

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  Explanation

  Amazon SageMaker Model Monitor detects data drift and model performance degradation in production and can trigger alerts when accuracy or data quality deviates from expected baselines.

• Question 318:

  An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question. Which solution meets these requirements with the LEAST implementation effort?

  A. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
  B. Add a role description to the prompt context that instructs the model of the age range that the response should target.
  C. Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
  D. Summarize the response text depending on the age of the user so that younger users receive shorter responses.
  B. Add a role description to the prompt context that instructs the model of the age range that the response should target.

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  Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.

  Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target":This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.

  Option A:"Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.

  Option C:"Use chain-of- thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.

  Option D:"Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.

  References:

  Prompt Engineering Techniques on AWS:AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.

• Question 319:

  A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in. Which data governance strategy will ensure compliance and protect patient privacy?

  A. Data residency
  B. Data quality
  C. Data discoverability
  D. Data enrichment
  A. Data residency

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  Data residency refers to the practice of ensuring that data is stored and processed within a specific geographical location to comply with regulations or policies. In this scenario, where sensitive patient data must not leave the country it originates from, implementing a data residency strategy ensures compliance with legal and regulatory requirements while protecting patient privacy. This approach is crucial for organizations like hospitals operating under strict data governance frameworks such as HIPAA or GDPR.

• Question 320:

  Which AWS service or feature stores embeddings in a vector database for use with foundation models (FMs) and Retrieval Augmented Generation (RAG)?

  A. Amazon SageMaker Ground Truth
  B. Amazon OpenSearch Service
  C. Amazon Transcribe
  D. Amazon Textract
  B. Amazon OpenSearch Service

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  Amazon OpenSearch Service provides a vector database capability to store and search embeddings, which are essential for powering Retrieval Augmented Generation (RAG) workflows and integrating with foundation models (FMs) in generative AI solutions.