Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 91:

  HOTSPOT

  A company uses ML techniques to build applications.

  Select the correct ML technique from the following list for each task. Select each ML technique one time.

  

  

  Explanation:

  Analyze a text question to determine if the answer is correct -> Binary classification Analyze ecological factors to determine the number of species in a certain area -> Regression Analyze car attributes to determine the car model -> Multiclass classification

  Determining if an answer is correct is a yes/no (binary) decision.

  Predicting the number of species (a numeric value) is a regression task.

  Identifying a car model from attributes involves choosing from multiple possible classes, which is multiclass classification.

• Question 92:

  A company uses an Amazon Bedrock foundation model (FM) to summarize documents for an internal use case. The company trained a custom model in Amazon Bedrock to improve the quality of the model's summarizations. The company needs a solution to use the customized model on Amazon Bedrock.

  Which solution will meet this requirement?

  A. Purchase Provisioned Throughput for the custom model.
  B. Deploy the custom model in an Amazon SageMaker AI endpoint for real-time inference.
  C. Register the model with the Amazon SageMaker Model Registry.
  D. Update the approval status of the model version to Approved.
  A. Purchase Provisioned Throughput for the custom model.

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  Explanation

  Customized foundation models in Amazon Bedrock require Provisioned Throughput to be purchased so the model can be deployed and used for inference, enabling the application to access and run the custom summarization model.

• Question 93:

  A company trains image and text generation models on Amazon SageMaker AI. The company releases the models by using Amazon Bedrock. The company must retain a tamper-proof, queryable record of every API call from SageMaker AI, Amazon Bedrock, and AWS Identity and Access Management (IAM).

  Which AWS service will meet these requirements?

  A. AWS Trusted Advisor
  B. Amazon Macie
  C. AWS CloudTrail Lake
  D. Amazon Inspector
  C. AWS CloudTrail Lake

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  Explanation

  AWS CloudTrail Lake provides a tamper-proof, immutable, and queryable event data store that records API activity across services such as Amazon SageMaker AI, Amazon Bedrock, and IAM, enabling long-term auditing, investigation, and compliance reporting.

• Question 94:

  Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?

  A. Calculate the total cost of resources used by the model.
  B. Measure the model's accuracy against a predefined benchmark dataset.
  C. Count the number of layers in the neural network.
  D. Assess the color accuracy of images processed by the model.
  B. Measure the model's accuracy against a predefined benchmark dataset.

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  Measuring the model's accuracy against a predefined benchmark dataset is the correct strategy to evaluate the accuracy of a foundation model (FM) used in image classification tasks.

• Question 95:

  A company is developing a mobile ML app that uses a phone's camera to diagnose and treat insect bites. The company wants to train an image classification model by using a diverse dataset of insect bite photos from different genders, ethnicities, and geographic locations around the world. Which principle of responsible AI does the company demonstrate in this scenario?

  A. Fairness
  B. Explainability
  C. Governance
  D. Transparency
  A. Fairness

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  The company is actively seeking to ensure that the image classification model is trained on a diverse dataset that includes insect bite photos from various genders, ethnicities, and geographic locations. This reflects the fairness principle of responsible AI, which emphasizes creating models that make unbiased decisions across all demographic groups. By including a diverse range of data, the company is aiming to prevent biases that could lead to inaccurate diagnoses or treatments for certain groups of people.

  Fairness ensures that AI systems do not discriminate based on race, gender, geography, or other characteristics.

• Question 96:

  An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model. Which technique will solve the problem?

  A. Data augmentation for imbalanced classes
  B. Model monitoring for class distribution
  C. Retrieval Augmented Generation (RAG)
  D. Watermark detection for images
  A. Data augmentation for imbalanced classes

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  Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.

• Question 97:

  A company needs to allow an application in a VPC to privately access AWS AI services without sending traffic over the public internet.

  Which AWS feature should the company use?

  A. Internet gateway
  B. AWS PrivateLink
  C. Amazon CloudFront
  D. Amazon Route 53
  B. AWS PrivateLink

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  Explanation

  AWS PrivateLink is the correct answer because it enables private connectivity from a VPC to supported AWS services without exposing traffic to the public internet.

  Option B (Correct): "AWS PrivateLink": This is correct because it provides private network access to supported services.

  Option A: "Internet gateway" is incorrect because it enables internet connectivity.

  Option C: "Amazon CloudFront" is incorrect because it is a content delivery service.

  Option D: "Amazon Route 53" is incorrect because it is a DNS service.

• Question 98:

  A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?

  A. Supervised learning
  B. Unsupervised learning
  C. Reinforcement learning
  D. Active learning
  A. Supervised learning

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  In supervised learning, the model is trained using a labeled dataset, where each image (input) has a corresponding label (the type of animal, in this case). Since the company already has a large dataset of labeled images, supervised learning is the most appropriate approach. The model learns to classify the images based on the features and labels in the training data.

• Question 99:

  A company is deploying an AI-powered loan approval system. The company must comply with data governance regulations for AI.

  Which solution will meet these requirements?

  A. Modify AI outputs based on user preferences without audit trails.
  B. Implement data lifecycle management to track and manage AI training data.
  C. Prioritize AI inference time optimization over data residency requirements.
  D. Use only synthetic data for model training to avoid compliance risks.
  B. Implement data lifecycle management to track and manage AI training data.

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  Explanation

  Implementing data lifecycle management helps track how training data is collected, stored, used, updated, and retained, which supports compliance with AI data governance requirements and provides accountability over the data used by the loan approval system.

• Question 100:

  An AI practitioner wants to generate more diverse and more creative outputs from a large language model (LLM). How should the AI practitioner adjust the inference parameter?

  A. Increase the temperature value.
  B. Decrease the Top K value.
  C. Increase the response length.
  D. Decrease the prompt length.
  A. Increase the temperature value.

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  Raising the temperature softens the model's probability distribution, increasing the chance of sampling less-likely tokens and thus producing more varied and creative responses.