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[General] Free PDF Quiz 2026 Amazon The Best AIF-C01: Valid AWS Certified AI Practitioner

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【General】 Free PDF Quiz 2026 Amazon The Best AIF-C01: Valid AWS Certified AI Practitioner

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Amazon AWS Certified AI Practitioner Sample Questions (Q253-Q258):NEW QUESTION # 253
A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again.
Which solution will meet these requirements?
  • A. Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
  • B. Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
  • C. Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
  • D. Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
Answer: C
Explanation:
The company needs to address the degradation in model inference quality after 4 months in production and prevent future occurrences by receiving notifications. Retraining the model can address the current degradation, likely caused by data drift (changes in the data distribution over time). Amazon SageMaker Model Monitor is designed to detect and monitor model drift, alerting the company when inference quality degrades, thus meeting both requirements.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Monitor enables you to monitor machine learning models in production for data drift, model performance degradation, and other quality issues. It can detect drift in feature distributions and inference quality, sending notifications when deviations are detected, allowing you to take corrective actions such as retraining the model." (Source: Amazon SageMaker Developer Guide, Monitoring Models with SageMaker Model Monitor) Detailed Explanation:
* Option A: Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
SageMaker Clarify is used for bias detection and explainability, not for monitoring model drift or inference quality in production. This option does not fully meet the requirements.
* Option B: Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
This is the correct answer. Retraining addresses the current degradation, and SageMaker Model Monitor can detect future drift in inference quality, sending notifications to prevent recurrence, as required.
* Option C: Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
SageMaker Feature Store is for managing and sharing features, not for monitoring model drift or inference quality. Building a new model may not be necessary if retraining can address the issue.
* Option D: Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
SageMaker JumpStart provides pre-trained models and solutions for quick deployment, but it does not offer specific tools for monitoring model drift or inference quality in production.
References:
Amazon SageMaker Developer Guide: Monitoring Models with SageMaker Model Monitor (https://docs.aws.
amazon.com/sagemaker/latest/dg/model-monitor.html)
AWS AI Practitioner Learning Path: Module on Model Monitoring and Maintenance AWS Documentation: Addressing Model Drift in Production (https://aws.amazon.com/sagemaker/)

NEW QUESTION # 254
A company needs to build its own large language model (LLM) based on only the company's private dat a. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?
  • A. Amazon EC2 P series
  • B. Amazon EC2 C series
  • C. Amazon EC2 G series
  • D. Amazon EC2 Trn series
Answer: D
Explanation:
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.
Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.
Option C: "Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner Reference:
AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.

NEW QUESTION # 255
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
  • A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
  • B. Encrypt the confidential data in the inference responses by using Amazon SageMaker.
  • C. Mask the confidential data in the inference responses by using dynamic data masking.
  • D. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
Answer: A
Explanation:
When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
Option A (Correct): "Delete the custom model. Remove the confidential data from the training dataset.
Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.
Option B: "Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database.
It does not address the core problem of the model beingtrained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training.
Option C: "Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process.
Option D: "Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.
AWS AI Practitioner References:
Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.
Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.

NEW QUESTION # 256
A company wants to build and deploy ML models on AWS without writing any code.
Which AWS service or feature meets these requirements?
  • A. Amazon Comprehend
  • B. AWS DeepRacer
  • C. Amazon Rekognition
  • D. Amazon SageMaker Canvas
Answer: D
Explanation:
Amazon SageMaker Canvas is a visual, no-code tool for building and deploying ML models. According to the official SageMaker Canvas documentation:
Explanation:
Amazon SageMaker Canvas is a visual, no-code tool for building and deploying ML models. According to the official SageMaker Canvas documentation:
"SageMaker Canvas provides a visual point-and-click interface that allows business analysts to generate accurate ML predictions without writing any code."

NEW QUESTION # 257
A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.
  • A. Fairness
  • B. Data governance
  • C. Privacy and security
  • D. Explainability
Answer: D
Explanation:
The correct answer is A - Explainability. According to AWS Responsible AI documentation, explainability refers to an AI system's ability to clearly communicate why it produced a given result. In healthcare, clinical decision support systems must provide traceable, understandable reasoning, especially when generating treatment recommendations. AWS highlights explainability as critical for high-impact domains such as medicine because doctors and patients must trust and understand the basis for AI-driven decisions. Tools like Amazon SageMaker Clarify support feature attribution, helping clinicians understand which patient factors (e.
g., age, symptoms, lab values) influenced a recommendation. Privacy and security (B) protect data but do not provide rationale. Fairness (C) ensures equitable treatment across demographics but does not explain decisions. Data governance (D) focuses on handling and controlling data, not model decision transparency.
Explainability is the AWS principle that ensures clinical users can interpret, validate, and rely on AI- generated recommendations for patient care.
Referenced AWS Documentation:
* AWS Responsible AI Whitepaper - Explainability
* Amazon SageMaker Clarify - Feature Attribution and Model Insights

NEW QUESTION # 258
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