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AIF-C01 Valid Study Questions - Exam AIF-C01 Learning

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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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 3

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 4

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 5

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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q69-Q74):

NEW QUESTION # 69
Which option is a use case for generative AI models?

A. Creating photorealistic images from text descriptions for digital marketing
B. Improving network security by using intrusion detection systems
C. Analyzing financial data to forecast stock market trends
D. Enhancing database performance by using optimized indexing

Answer: A

NEW QUESTION # 70
How can companies use large language models (LLMs) securely on Amazon Bedrock?

A. Enable AWS Audit Manager for automatic model evaluation jobs.
B. Enable Amazon Bedrock automatic model evaluation jobs.
C. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
D. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.

Answer: D

Explanation:
To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.
* Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management.
* Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.
* Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.
* Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.
AWS AI Practitioner References:
* Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.

NEW QUESTION # 71
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?

A. Add hyperparameters to the model.
B. Reduce the volume of data that is used in training.
C. Increase the volume of data that is used in training.
D. Increase the model training time.

Answer: C

Explanation:
When a model performs well on the training data but poorly in production, it is often due to overfitting.
Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
* Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
* Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
* Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
* Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.
AWS AI Practitioner References:
* Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.

NEW QUESTION # 72
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. Toxicity
B. Privacy
C. Hallucinations
D. Plagiarism

Answer: D

NEW QUESTION # 73
An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.
Which strategy meets these requirements?

A. Named entity recognition
B. Object detection
C. Anomaly detection
D. Inpainting

Answer: B

Explanation:
Object detection is the correct strategy for automatically identifying and categorizing animals in photos.
* Object Detection:
* A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.
* Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.
* Why Option A is Correct:
* Automatic Identification: Object detection models can automatically identify different types of animals in the images without manual intervention.
* Categorization Capability: Assigns labels to detected objects, fulfilling the requirement for categorizing animals.
* Why Other Options are Incorrect:
* B. Anomaly detection: Identifies outliers or unusual patterns, not specific objects in images.
* C. Named entity recognition: Used in NLP to identify entities in text, not for image processing.
* D. Inpainting: Used for filling in missing parts of images, not for detecting or categorizing objects.

NEW QUESTION # 74
......

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Tom Wilson Tom Wilson

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