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VCE AIF-C01 Dumps | AIF-C01 Valid Exam Answers

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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.

Topic 2

Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.

Topic 3

Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

Topic 4

Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.

Topic 5

Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.

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Amazon AWS Certified AI Practitioner Sample Questions (Q130-Q135):

NEW QUESTION # 130
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 JumpStart.
B. Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
C. Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
D. Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.

Answer: D

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 # 131
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?

A. Hallucinations
B. Privacy
C. Toxicity
D. Plagiarism

Answer: D

Explanation:
The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.
* Plagiarism:
* Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.
* This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.
* Why Option C is Correct:
* Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.
* Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.
* Why Other Options are Incorrect:
* A. Toxicity: Refers to harmful or offensive content generation, not content copying.
* B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.
* D. Privacy: Involves the misuse or exposure of personal information, not copying content.

NEW QUESTION # 132
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?

A. Recall
B. Lift chart
C. Accuracy
D. Precision

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)
"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 # 133
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?

A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
B. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
C. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.
D. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.

Answer: A

Explanation:
To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.
* Custom Service Role Per Team:
* A custom service role for each team ensures that the access control is granular, allowing only specific teams to access their own customer data in Amazon S3.
* This setup aligns with the principle of least privilege, ensuring teams can only interact with data they are authorized to access.
* Why Option A is Correct:
* Access Control: Allows precise access permissions for each team's data.
* Security Compliance: Directly meets the company's security policy requirements by ensuring data segregation.
* Why Other Options are Incorrect:
* B. Custom service role with customer name specification: This approach is impractical as it relies on manual input, which is prone to errors and does not inherently enforce data access controls.
* C. Redacting personal data and updating S3 bucket policy: Redaction does not solve the requirement for team-specific access, and updating bucket policies is less granular than creating roles.
* D. One Bedrock role with full S3 access and IAM roles for teams: This setup does not meet the least privilege principle, as having a single role with full access is contrary to the company's security policy.
Thus, A is the correct answer to meet the company's security requirements.

NEW QUESTION # 134
A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?

A. XGBoost
B. Generative adversarial network (GAN)
C. Residual neural network
D. WaveNet

Answer: B

Explanation:
Generative adversarial networks (GANs) are a type of deep learning model used for generating synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a discriminator) that work together to create realistic data.
* Option A (Correct): "Generative adversarial network (GAN)": This is the correct answer because GANs are specifically designed for generating synthetic data that closely resembles the real data they are trained on.
* Option B: "XGBoost" is a gradient boosting algorithm for classification and regression tasks, not for generating synthetic data.
* Option C: "Residual neural network" is primarily used for improving the performance of deep networks, not for generating synthetic data.
* Option D: "WaveNet" is a model architecture designed for generating raw audio waveforms, not synthetic data in general.
AWS AI Practitioner References:
* GANs on AWS for Synthetic Data Generation: AWS supports the use of GANs for creating synthetic datasets, which can be crucial for applications like training machine learning models in environments where real data is scarce or sensitive.

NEW QUESTION # 135
......

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