Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 231:

  A company wants to implement a generative AI solution to improve its marketing operations. The company wants to increase its revenue in the next 6 months. Which approach will meet these requirements?

  A. Immediately start training a custom FM by using the company's existing data.
  B. Conduct stakeholder interviews to refine use cases and set measurable goals.
  C. Implement a prebuilt AI assistant solution and measure its impact on customer satisfaction.
  D. Analyze industry AI implementations and replicate the most successful features.
  C. Implement a prebuilt AI assistant solution and measure its impact on customer satisfaction.

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  Using a prebuilt AI assistant allows the company to deploy generative AI quickly without the time and cost of training custom models. This accelerates implementation, enabling measurable business impact such as improved customer engagement and increased revenue within the short 6-month timeframe.

• Question 232:

  A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data. Which solution will resolve this issue?

  A. Decrease the regularization parameter to increase model complexity.
  B. Increase the regularization parameter to decrease model complexity.
  C. Add more features to the input data.
  D. Train the model for more epochs.
  B. Increase the regularization parameter to decrease model complexity.

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  The issue described is a common case of overfitting, where the model performs well on the training data but fails to generalize to new, unseen data. This suggests that the model is too complex and has learned to memorize the training data rather than identifying generalizable patterns. Increasing the regularization parameter helps to reduce model complexity by penalizing large weights, thereby encouraging simpler models that are less likely to overfit. This will improve the model's ability to generalize to new data, potentially improving performance on unseen customer data.

• Question 233:

  A company wants to create an AI solution to generate images and descriptions for a product catalog. The company needs to select a foundation model (FM) for this solution.

  The company must consider the output types of each FM.

  Which FM characteristic is the company evaluating?

  A. Latency
  B. Model size
  C. Model customization
  D. Modality
  D. Modality

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  Modality refers to the types of input and output data a foundation model can handle, such as text, images, or both. Evaluating modality ensures the selected FM can generate both images and text descriptions for the product catalog.

• Question 234:

  A company is using Amazon SageMaker AI to develop AI/ML solutions. The company must use only approved data for model training. The AI/ML solutions must comply with company policy and ethical guidelines.

  Which solution will meet these requirements?

  A. Amazon SageMaker Catalog
  B. Amazon SageMaker Clarify
  C. Amazon SageMaker Model Registry
  D. Amazon SageMaker Model Cards
  A. Amazon SageMaker Catalog

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  Explanation

  Amazon SageMaker Catalog enforces governance and data access controls by allowing organizations to manage, approve, and control which datasets and assets are available for model training, ensuring that only approved data is used and that development complies with company policies and ethical guidelines.

• Question 235:

  A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics. Which ML strategy should the company use to meet these requirements?

  A. Unsupervised learning
  B. Supervised learning
  C. Reinforcement learning
  D. Semi-supervised learning
  A. Unsupervised learning

• Question 236:

  A company must comply with regulatory standards to develop and use trustworthy AI management solutions.

  Which approach will meet this requirement?

  A. Optimize model inference time by using high-powered GPUs for faster processing.
  B. Ensure that each AI solution is developed only by technical experts. Do not involve other stakeholders.
  C. Constrain transparency and user access to each model's decision-making process.
  D. Ensure fairness, transparency, accountability, and security throughout the lifecycle of each AI solution.
  D. Ensure fairness, transparency, accountability, and security throughout the lifecycle of each AI solution.

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  Explanation

  Complying with trustworthy AI regulations requires embedding fairness, transparency, accountability, and security across the entire AI lifecycle, from design and development through deployment and ongoing operation.

• Question 237:

  A company needs to collect a large dataset to train an AI assistant in a specific content area. Which dataset will meet this requirement?

  A. Diverse conversations that use relevant terminology
  B. Time series data of general purpose historical sales
  C. Sentiment analysis of news articles
  D. Unique product IDs and corresponding user IDs
  A. Diverse conversations that use relevant terminology

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  To train an AI assistant for a specific content area, you need a large dataset of diverse conversations that incorporate the relevant terminology and context for that area. This enables the assistant to learn how to respond accurately and appropriately within the targeted subject.

• Question 238:

  Which prompting technique can protect against prompt injection attacks?

  A. Adversarial prompting
  B. Zero-shot prompting
  C. Least-to-most prompting
  D. Chain-of-thought prompting
  A. Adversarial prompting

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  Adversarial prompting is a technique used to defend against prompt injection attacks by crafting inputs that are specifically designed to identify and neutralize malicious prompts. This approach involves generating prompts that can detect and mitigate adversarial inputs, thereby enhancing the robustness of language models against such attacks.

• Question 239:

  A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights. Which AWS service can the company use to meet this requirement?

  A. Amazon Lex
  B. Amazon Comprehend
  C. Amazon Transcribe
  D. Amazon Translate
  B. Amazon Comprehend

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  Amazon Comprehend is the correct service to analyze customer support interactions and identify frequently asked questions and insights.

• Question 240:

  A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls. Which solution meets these requirements?

  A. Build a conversational chatbot by using Amazon Lex.
  B. Transcribe call recordings by using Amazon Transcribe.
  C. Extract information from call recordings by using Amazon SageMaker Model Monitor.
  D. Create classification labels by using Amazon Comprehend.
  B. Transcribe call recordings by using Amazon Transcribe.

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  Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.