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NEW QUESTION # 155
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?
Answer: D
Explanation:
In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.
* Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.
* Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.
* Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.
* Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.
AWS AI Practitioner References:
* Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.
NEW QUESTION # 156
An AI practitioner is developing a prompt for large language models (LLMs) in Amazon Bedrock. The AI practitioner must ensure that the prompt works across all Amazon Bedrock LLMs.
Which characteristic can differ across the LLMs?
Answer: C
Explanation:
The correct answer is A because each foundation model on Amazon Bedrock (e.g., Claude, Titan, Mistral, Meta Llama) has a different maximum token limit, which defines the maximum number of tokens the model can accept in the prompt and generate in the response.
From AWS documentation:
"Each model in Amazon Bedrock has its own maximum token limit. Prompts exceeding the limit must be truncated or adjusted depending on the selected model." Explanation of other options:
B). On-demand inference support is a platform feature that is uniformly supported across models on Bedrock.
C). All Bedrock LLMs support randomness control through temperature and top-p parameters.
D). Amazon Bedrock Guardrails are designed to work across supported models, though specific behaviors may vary slightly.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Model Comparison Guide
* AWS Prompt Engineering and LLM Deployment Documentation
* AWS ML Specialty Study Guide - Bedrock Model Capabilities
NEW QUESTION # 157
A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.
Which combination of AWS service and storage class meets these requirements? (Select TWO.)
Answer: C,E
Explanation:
AWS CloudTrail: Logs all API calls to Amazon Bedrock.
Amazon S3 Intelligent-Tiering: Optimizes storage costs for long-term retention with automatic tiering.
According to Amazon Bedrock Logging Documentation:
"CloudTrail records API activity and events, and logs can be stored in S3. For cost optimization, use S3 Intelligent-Tiering to retain logs long-term."
NEW QUESTION # 158
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?
Answer: B
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 # 159
A company has developed a neural network model to replace an existing decision tree model. The neural network model has a higher prediction accuracy compared to the decision tree model. However, the neural network model's decision process is not as explainable as the decision tree model's decision process.
Which tradeoff is the company making by adopting the neural network model?
Answer: C
Explanation:
The verified answer is C. Higher performance for lower interpretability . The question states that the neural network model has higher prediction accuracy than the decision tree model, but its decision process is less explainable. In machine learning, this is a classic tradeoff between predictive performance and interpretability. AWS machine learning guidance discusses the tradeoff between performance and model interpretability and explains that interpretability becomes especially important when there is a high cost for incorrect predictions or when stakeholders need to understand decisions made by the model.
A decision tree is generally more interpretable because its decision path can often be traced through visible splits and rules. A neural network, especially a deeper model, often captures complex nonlinear relationships and can achieve higher prediction accuracy, but its internal decision process is usually harder to explain directly. AWS SageMaker Clarify documentation supports this distinction by explaining that model explainability tools help explain how machine learning models make predictions and help stakeholders understand model characteristics before deployment and debug predictions after deployment.
Option A is incorrect because lower interpretability does not produce higher compliance. In regulated environments, lower interpretability can create more governance and compliance challenges, not fewer.
Option B is incorrect because portability is not the issue described. The question does not discuss moving the model across environments or platforms. Option D is incorrect for the same reason: portability is unrelated to the stated facts.
The company is choosing the model with better predictive accuracy, which means higher model performance.
But it is accepting a weaker ability to explain how the model reaches its predictions. That is why the correct tradeoff is higher performance for lower interpretability .
NEW QUESTION # 160
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