NEWEST EXAM AIF-C01 TOPIC COVERS THE ENTIRE SYLLABUS OF AIF-C01

Newest Exam AIF-C01 Topic Covers the Entire Syllabus of AIF-C01

Newest Exam AIF-C01 Topic Covers the Entire Syllabus of AIF-C01

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Amazon AWS Certified AI Practitioner Sample Questions (Q43-Q48):

NEW QUESTION # 43
A company is implementing the Amazon Titan foundation model (FM) by using 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?

  • A. Create an Amazon Bedrock knowledge base
  • B. Enable model invocation logging
  • C. Choose a lower temperature value
  • D. Use a different FM

Answer: A


NEW QUESTION # 44
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. Develop a summarization chatbot.
  • B. Develop a multi-language translation system.
  • C. Create a recommendation engine.
  • D. Build an automatic named entity recognition system.

Answer: A


NEW QUESTION # 45
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?

  • A. Customize the model by using fine-tuning.
  • B. Use Provisioned Throughput.
  • C. Decrease the number of tokens in the prompt.
  • D. Increase the number of tokens in the prompt.

Answer: C


NEW QUESTION # 46
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?

  • A. BERT-based models
  • B. Topic modeling
  • C. Clustering models
  • D. Prescriptive ML models

Answer: A

Explanation:
BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
* BERT-based Models:
* BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
* It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
* Why Option D is Correct:
* Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
* Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
* Why Other Options are Incorrect:
* A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
* B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
* C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.


NEW QUESTION # 47
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?

  • A. Supervised learning with a manually curated dataset of good responses and bad responses
  • B. Unsupervised learning to find clusters of similar customer inquiries
  • C. Reinforcement learning with rewards for positive customer feedback
  • D. Supervised learning with a continuously updated FAQ database

Answer: C


NEW QUESTION # 48
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