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Free Amazon AIF-C01 Exam Braindumps (page: 6)

Page 6 of 48
PDF with 233 Questions

Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

  1. Integration with Amazon S3 for object storage
  2. Support for geospatial indexing and queries
  3. Scalable index management and nearest neighbor search capability
  4. Ability to perform real-time analysis on streaming data

Answer(s): C

Explanation:

The scalable index management and nearest neighbor search capability in Amazon OpenSearch Service enables companies to build vector database applications, which are crucial for tasks like similarity search in AI models. The other options do not specifically provide the vector search functionality.



Which option is a use case for generative AI models?

  1. Improving network security by using intrusion detection systems
  2. Creating photorealistic images from text descriptions for digital marketing
  3. Enhancing database performance by using optimized indexing
  4. Analyzing financial data to forecast stock market trends

Answer(s): B

Explanation:

Generative AI models are used to create new content, such as photorealistic images from text descriptions, which is useful for digital marketing. The other options involve tasks better suited for analytical or detection systems rather than generative models.



A company wants to build a generative AI application by using Amazon Bedrock and needs to choose a foundation model (FM). The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?

  1. Temperature
  2. Context window
  3. Batch size
  4. Model size

Answer(s): B

Explanation:

The context window determines how much information can fit into a single prompt. It specifies the number of tokens the foundation model can process at once, affecting the length of input that can be provided. The other options do not directly relate to prompt size.



A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.
The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?

  1. Set a low limit on the number of tokens the FM can produce.
  2. Use batch inferencing to process detailed responses.
  3. Experiment and refine the prompt until the FM produces the desired responses.
  4. Define a higher number for the temperature parameter.

Answer(s): C

Explanation:

Experimenting and refining the prompt allows you to guide the FM to produce responses that align with the company's desired tone. This approach helps to shape the behavior of the chatbot. The other options do not directly ensure adherence to company tone.



A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?

  1. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
  2. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
  3. Provide the new text passage to be classified without any additional context or examples.
  4. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.

Answer(s): A

Explanation:

Providing examples with labels in the prompt helps the LLM understand the context of sentiment analysis, improving its accuracy in classifying the new text passage as positive or negative. The other options do not effectively guide the LLM for sentiment analysis.



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