Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 331:

  HOTSPOT

  A company wants to use Amazon SageMaker features for various use cases.

  Select the correct SageMaker feature from the following list for each use case. Each SageMaker feature should be selected one time or not at all.

  

  

  Explanation:

• Question 332:

  A company wants to implement a single environment for both data and AI development. Developers across different teams must be able to access the environment and work together. The developers must be able to build and share models and generative AI applications securely in the environment.

  Which AWS solution will meet these requirements?

  A. Amazon Lex
  B. Amazon SageMaker Unified Studio
  C. Amazon Bedrock PartyRock
  D. Amazon Q Developer
  B. Amazon SageMaker Unified Studio

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  Explanation

  Amazon SageMaker Unified Studio provides a collaborative, centralized environment for data and AI development where multiple teams can securely build, share, and manage models and generative AI applications.

• Question 333:

  A company is working on a large language model (LLM) and noticed that the LLM's outputs are not as diverse as expected. Which parameter should the company adjust?

  A. Temperature
  B. Batch size
  C. Learning rate
  D. Optimizer type
  A. Temperature

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  The "temperature" parameter controls the randomness and diversity of outputs generated by a large language model. Increasing the temperature produces more diverse and creative responses, while lowering it makes the outputs more focused and deterministic.

• Question 334:

  A financial company uses AWS to host its generative AI models. The company must generate reports to show adherence to international regulations for handling sensitive customer data. Which AWS service meets these requirements?

  A. Amazon Macie
  B. AWS Artifact
  C. AWS Secrets Manager
  D. AWS Config
  B. AWS Artifact

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  AWS Artifact provides on-demand access to AWS's compliance reports and certifications (for example, ISO, SOC, GDPR), enabling the company to demonstrate its generative AI workloads and data handling practices adhere to international regulatory requirements.

• Question 335:

  A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network. Which solution will meet these requirements?

  A. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.
  B. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.
  C. Use AWS PrivateLink to connect the VPC and Amazon Bedrock.
  D. Use AWS Key Management Service (AWS KMS) keys to encrypt the data.
  C. Use AWS PrivateLink to connect the VPC and Amazon Bedrock.

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  AWS PrivateLink enables secure, private connectivity between your VPC and Amazon Bedrock without exposing data to the public internet, ensuring that sensitive data stays within your private network during fine-tuning.

• Question 336:

  A company wants to detect unauthorized API actions for foundation models (FMs) in Amazon Bedrock.

  Which solution meets these requirements?

  A. Configure AWS CloudTrail to monitor API activity.
  B. Use AWS Trusted Advisor to monitor model input and output data.
  C. Create Amazon QuickSight dashboards to monitor row-level safety.
  D. Monitor user access by using Amazon Personalize.
  A. Configure AWS CloudTrail to monitor API activity.

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  Explanation

  AWS CloudTrail records and monitors Amazon Bedrock API activity, which helps detect unauthorized API actions performed against foundation models.

• Question 337:

  An ecommerce company receives multiple gigabytes of customer data daily. The company uses the data to train an ML model to forecast future product demand. The company needs a solution to perform inferences once each day. Which inference type meets these requirements?

  A. Batch inference
  B. Asynchronous inference
  C. Real-time inference
  D. Serverless inference
  A. Batch inference

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  Batch inference is designed for high-volume, scheduled predictions: you can point it at the day's gigabytes of data, run a job once daily, and generate all forecasts in one go without needing a persistent endpoint.

• Question 338:

  Which task describes a use case for intelligent document processing (IDP)?

  A. Predict fraudulent transactions
  B. Personalize product offerings
  C. Analyze user feedback and perform sentiment analysis
  D. Automatically extract and format data from scanned files
  D. Automatically extract and format data from scanned files

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  Explanation

  Intelligent document processing is designed to extract, classify, and structure data from unstructured documents such as scanned files, PDFs, and images using OCR and AI, enabling automatic data extraction and formatting for downstream systems.

• Question 339:

  A manufacturing company wants to create product descriptions in multiple languages. Which AWS service will automate this task?

  A. Amazon Translate
  B. Amazon Transcribe
  C. Amazon Kendra
  D. Amazon Polly
  A. Amazon Translate

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  Amazon Translate is an AWS service that automates language translation using neural machine translation (NMT). It enables businesses to generate product descriptions in multiple languages quickly and accurately, making it the best choice for this task.

• Question 340:

  A company wants to integrate an AI solution to contact emergency services within 30 seconds of vehicle. crash detection. The company wants to use a pre-trained model without additional training.

  Which factor should the company prioritize when selecting a model to meet these requirements?

  A. Model customization
  B. Model size
  C. Model cost
  D. Model temperature
  B. Model size

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  Explanation

  Model size directly affects inference speed and latency, which is critical for meeting strict real-time response requirements such as contacting emergency services within 30 seconds using a pre-trained model.