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Amazon AWS Certified AI Practitioner Sample Questions (Q23-Q28):

NEW QUESTION # 23
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?

A. Provide labeled data with the prompt field and the completion field.
B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
C. Purchase Provisioned Throughput for Amazon Bedrock.
D. Train the model on journals and textbooks.

Answer: A

Explanation:
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
* Fine-Tuning Strategy:
* To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
* This allows the model to learn the desired behavior or response style based on the provided examples.
* Why Option A is Correct:
* Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
* Customization: Ensures that the model is fine-tuned to the specific requirements of the company' s data and desired outputs.
* Why Other Options are Incorrect:
* B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
* C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
* D. Train on journals and textbooks: Lacks the specific format and labeling required for fine- tuning.

NEW QUESTION # 24
A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team.
Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times. (Select THREE.)
* Batch transform
* Real-time inference

Answer:

Explanation:

Explanation:
Use Case 1:
The company's chatbot needs predictions from the LLM to understand users' intent with minimal latency.
# answer: Real-time inference
Chatbots require low-latency, immediate responses to user input. Real-time inference is ideal for these interactive use cases.
Use Case 2:
A data processing job needs to query the LLM to process gigabytes of text files on weekends.
# answer: Batch transform
Batch transform is designed for asynchronous, high-throughput jobs where latency is not critical. It's ideal for scheduled or large-scale processing like weekend batch jobs on large datasets.
Use Case 3:
The company's engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions.
Real-time inference
An API requiring fast response time for small content sizes is best served by real-time inference to meet latency requirements.

NEW QUESTION # 25
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. Optimizer type
B. Temperature
C. Batch size
D. Learning rate

Answer: B

Explanation:
The correct answer is A because temperature controls the randomness of a language model's output. A higher temperature increases diversity by making the model more likely to explore less probable tokens, while a lower temperature results in more deterministic and repetitive outputs.
From AWS documentation:
"The temperature parameter in LLMs adjusts the randomness of generated responses. Higher values (e.g., 0.8-
1.0) produce more creative and diverse output, while lower values (e.g., 0.1-0.3) make output more focused and repetitive." Explanation of other options:
B). Batch size is related to training efficiency, not output diversity.
C). Learning rate affects the training convergence rate, not inference-time output variety.
D). Optimizer type is a training configuration that influences how the model learns during training, not diversity during inference.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock - Parameter Tuning Guide
* AWS Machine Learning Specialty Guide - LLM Inference Parameters

NEW QUESTION # 26
Which statement describes a generative AI use case for multimodal models?

A. Deploy multiple scalable and cost-effective versions of a model.
B. Process large amounts of data to train multiple models.
C. Process different data types, such as images, audio, and videos.
D. Write code in multiple programming languages.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Multimodal models are designed to process and reason across multiple data modalities, such as text, images, audio, and video.
AWS generative AI guidance defines multimodal use cases as those where:
* Inputs come from different data types
* The model combines visual, textual, or audio understanding
* Outputs are generated based on combined context
Why the other options are incorrect:
* A describes deployment strategy, not multimodality.
* B describes training scale, not model capability.
* C is a coding use case, not multimodal processing.
AWS AI document references:
* Multimodal Foundation Models on AWS
* Generative AI Capabilities and Use Cases
* Building Multimodal Applications

NEW QUESTION # 27
HOTSPOT
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML

Answer:

Explanation:

Explanation:

Artificial Intelligence (AI) is the broad field focused on simulating human problem-solving and cognitive abilities, including reasoning, perception, and decision-making.
(Reference: AWS Certified AI Practitioner Official Study Guide)
Machine Learning (ML) is a subset of AI that uses data-driven algorithms to identify patterns and make predictions without explicit programming for each specific task.
(Reference: AWS Machine Learning Overview)
Deep learning is a subset of ML that uses neural networks with many layers (deep neural networks) to process complex data and extract high-level features.
(Reference: AWS Deep Learning on AWS)

NEW QUESTION # 28
......

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Valid Reliable AIF-C01 Study Guide offer you accurate Latest Guide Files | Amazon AWS Certified AI Practitioner

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Amazon AWS Certified AI Practitioner Sample Questions (Q23-Q28):

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