AIF-C01 Practice Test

An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.

Which strategy should the AI practitioner use?

A. Configure AWS CloudTrail as the logs destination for the model.

B. Enable invocation logging in Amazon Bedrock.

C. Configure AWS Audit Manager as the logs destination for the model.

D. Configure model invocation logging in Amazon EventBridge.

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.

A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.

Which solution meets these requirements?

A. Build an automatic named entity recognition system.

B. Create a recommendation engine.

C. Develop a summarization chatbot.

D. Develop a multi-language translation system.

An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.

Which type of FM should the AI practitioner use to power the search application?

A. Multi-modal embedding model

B. Text embedding model

C. Multi-modal generation model

D. Image generation model

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.

Which action must the company take to use the custom model through Amazon Bedrock?

A. Purchase Provisioned Throughput for the custom model.

B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

C. Register the model with the Amazon SageMaker Model Registry.

D. Grant access to the custom model in Amazon Bedrock.