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Amazon AIF-C01 Exam (page: 5)
Amazon AWS Certified AI Practitioner
Updated on: 12-Feb-2026

Viewing Page 5 of 43
AIF-C01 PDF 385 Questions

Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

  1. Amazon Personalize
  2. Amazon SageMaker JumpStart
  3. PartyRock, an Amazon Bedrock Playground
  4. Amazon SageMaker endpoints

Answer(s): B

Explanation:

Amazon SageMaker JumpStart provides pre-built models, including foundation models, that can be quickly deployed and consumed within a VPC, helping teams get started faster. The other options are not designed for deploying foundation models in this context.



How can companies use large language models (LLMs) securely on Amazon Bedrock?

  1. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
  2. Enable AWS Audit Manager for automatic model evaluation jobs.
  3. Enable Amazon Bedrock automatic model evaluation jobs.
  4. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.

Answer(s): A

Explanation:

Designing clear prompts and using IAM roles with least privilege access ensures secure use of LLMs on Amazon Bedrock by minimizing access risks and preventing misuse. The other options do not directly address securing the use of LLMs.



A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology.

Which solution meets these requirements?

  1. Generative pre-trained transformers (GPT)
  2. Residual neural network
  3. Support vector machine
  4. WaveNet

Answer(s): A

Explanation:

GPT models are well-suited for converting natural language input into structured queries like SQL, making them ideal for building an AI-based application that translates employee-provided text into SQL queries. The other options are not designed for natural language understanding and query generation tasks.



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.



Viewing Page 5 of 43



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