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Free Amazon AIF-C01 Exam Braindumps (page: 27)

Page 9 of 48
PDF with 233 Questions

A company built a deep learning model for object detection and deployed the model to production. Which AI process occurs when the model analyzes a new image to identify objects?

  1. Training
  2. Inference
  3. Model deployment
  4. Bias correction

Answer(s): B

Explanation:

Inference is the process where the model analyzes new data (in this case, a new image) to make predictions or identify objects. The other options are related to different stages of the AI lifecycle, such as building or preparing the model.



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?

  1. Data augmentation for imbalanced classes
  2. Model monitoring for class distribution
  3. Retrieval Augmented Generation (RAG)
  4. Watermark detection for images

Answer(s): A

Explanation:

Data augmentation for imbalanced classes helps address bias by creating a more balanced dataset, ensuring that different attributes are equally represented. This reduces bias in image generation. The other options do not directly address data bias issues.



A company is using an Amazon Titan foundation model (FM) in Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?

  1. Use a different FM.
  2. Choose a lower temperature value.
  3. Create an Amazon Bedrock knowledge base.
  4. Enable model invocation logging.

Answer(s): C

Explanation:

Creating an Amazon Bedrock knowledge base allows the company to supplement the foundation model with relevant data from their private data sources. This ensures that the model has access to the additional, context- specific information needed. The other options do not directly address supplementing the model with private data.



A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?

  1. Configure the security and compliance by using Amazon Inspector.
  2. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
  3. Encrypt and secure training data by using Amazon Macie.
  4. Gather more data. Use Amazon Rekognition to add custom labels to the data.

Answer(s): B

Explanation:

Amazon SageMaker Clarify helps with transparency and explainability by generating metrics, reports, and examples that show how the model makes decisions, which is essential for meeting regulatory requirements. The other options are not directly related to improving the model's transparency or explainability.



A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Choose two.)

  1. Auto scaling inference endpoints
  2. Threat detection
  3. Data protection
  4. Cost optimization
  5. Loosely coupled microservices

Answer(s): B,C

Explanation:

Threat detection: Ensuring security measures are in place to detect threats is important for compliance with regulatory frameworks.
Data protection: Proper data handling and protection measures are key compliance aspects, especially in applications dealing with sensitive customer information.
The other options (auto scaling, cost optimization, and loosely coupled microservices) are more related to performance and architecture rather than regulatory compliance.



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