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Title: TOP AIF-C01 Updated CBT 100% Pass | Latest Amazon Valid Test AWS Certified AI Pr [Print This Page]

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Title: TOP AIF-C01 Updated CBT 100% Pass | Latest Amazon Valid Test AWS Certified AI Pr
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Amazon AWS Certified AI Practitioner Sample Questions (Q295-Q300):NEW QUESTION # 295
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)
Answer: A,C
Explanation:
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.
Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.
Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner References:
Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

NEW QUESTION # 296
A company plans to use a generative AI model to provide real-time service quotes to users.
Which criteria should the company use to select the correct model for this use case?
Answer: A
Explanation:
The correct answer is D because low latency and optimized inference speed are critical for real-time applications. For delivering real-time service quotes, the system must respond in milliseconds or a few seconds at most, making latency a primary concern when choosing the model.
From AWS Bedrock documentation:
"When selecting a foundation model for real-time applications, inference speed and latency are key evaluation metrics to ensure responsive user experiences." Explanation of other options:
A . Model size affects performance and cost but doesn't directly guarantee low latency.
B . Training data quality is important for accuracy, but it doesn't address real-time performance requirements.
C . GPU availability matters in infrastructure planning, not in model selection for latency optimization.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Model Selection Guide - Real-time Use Case Considerations AWS ML Specialty Guide - Foundation Model Performance Criteria

NEW QUESTION # 297
A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent.
Which evaluation metric meets these requirements?
Answer: D
Explanation:
Precision is the metric that measures the proportion of true positives (actual frauds) among all flagged positives (flagged frauds). High precision ensures that most of the flagged cases are truly fraudulent, minimizing the number of false positives employees must review.
* C is correct:
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide)
* A (Recall) measures how many actual frauds are caught, but does not minimize false positives.
* B (Accuracy) can be misleading in imbalanced datasets (like fraud detection).

NEW QUESTION # 298
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning

Answer:
Explanation:


NEW QUESTION # 299
An AI practitioner is using Amazon Bedrock Prompt Management to create a reusable prompt. The prompt must be able to interact with external services by calling an external API. Which solution will meet this requirement?
Answer: B
Explanation:
The correct answer is B because Amazon Bedrock Prompt Management supports tool use via tools configuration, which enables a prompt to define tools that can invoke external APIs or services.
According to the AWS documentation:
"You can use the tools configuration in Amazon Bedrock to specify external APIs that a foundation model can call during inference. This enables the model to interact with external services, such as invoking functions, retrieving real-time data, or executing workflows." The tools configuration allows a prompt to describe which external functions (APIs) are available, their parameters, and how they should be invoked, similar to OpenAI's function calling or tool use pattern.

NEW QUESTION # 300
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