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retired do not use AI ML Test Questions Bank

do not use this - it is retired and replaced by

⁠

https://coda.io/@peter-sigurdson/building-the-ai-model-test-questions-bank⁠

Below are the questions designed to act as mini-lectures

Build Process:

PFEQ:

What do Operating Systems do:

1. Provides a Hypervisor, which is a Software layer that provides APIs to enable Application Software (Chrome, MS Word) to run and provide their services.

2 Provides the ability to create user accounts / password.

3. SAM Security access manager: Provisions a way to enable / deny user accounts from accessing system resources.

Thought Question: What is the difference between a Type 1 Bare Metal Hypervisor and an Operating System.

Type 1 Hypervisor: Bare Metal

Type 2: Lives as a guest with a HOST operating system.

What is the difference between a

Virtual Machines

Docker

Ansible

Learning Outcomes:

Build insight into the role of JSON schema in AI model engineering, particularly in handling big data and aiding AI models in learning from user interactions.

The correct answer for each question is marked with an asterisk (*).

Question 1:

What is the primary role of utilizing JSON schema in handling big data for AI applications?

a) Only for storing user data

b) Only for AI model evaluation

*c) Organizing and validating the structure of large datasets for efficient processing and training of AI models: being text, json data stores are easily input/output with PYTHON: One big win with JSON database schema: you can change the shape of the data container at runtime with program code.

d) Only for visual representation

Explanation:

JSON schema is primarily used to organize and validate the structure of large datasets in AI applications.

It ensures the data is in the correct format for efficient processing and training of AI models, contributing to the effective learning and functioning of AI systems, and allowing us to run CI / CD model training processes.

Question 2:

How does JSON schema assist in improving the conversational memory of an AI model?

a) By increasing the model's size

*b) By ensuring structured and consistent data storage and retrieval for learning from user interactions

c) By reducing the model's complexity

d) By focusing only on the graphical interface

Explanation:

JSON schema aids in enhancing the conversational memory of an AI model by ensuring structured and consistent data storage and retrieval. This consistency enables the AI model to effectively learn from user interactions, further refining its responses and interactions.

Question 3:

In the context of AI applications, how does JSON schema contribute to model engineering?

a) By only dealing with front-end interactions

b) By only handling model deployment

*c) By providing a standardized structure for data, facilitating efficient model training and development

d) By focusing only on cost reduction

Explanation:

JSON schema offers a standardized data structure, crucial for efficient model training and development in AI applications. A standardized and organized data format aids in seamless and effective model engineering, contributing to the building of robust AI systems.

Question 4:

Why is the consistent structure provided by JSON schema essential for AI models to learn from user interactions?

a) Only for improving visual elements

*b) It ensures reliable and orderly data storage, aiding in effective learning and memory retention for AI models

c) Only for enhancing security

d) Only for reducing computation time

Explanation:

A consistent data structure assured by JSON schema is pivotal as it guarantees reliable and orderly data storage. This organization is crucial for AI models to effectively learn and retain information from user interactions, enhancing their performance and response generation.

Question 5:

How does the use of JSON schema in AI model engineering align with big data processing?

a) Only for data deletion

*b) It aids in handling and processing large datasets efficiently, ensuring AI models have ample and structured data for training and learning

c) Only for data encryption

d) Only for improving data visualization

Explanation:

Utilizing JSON schema in AI model engineering is harmonious with big data processing as it assists in efficiently handling and processing large datasets. This efficiency ensures that AI models have access to ample and well-organized data for robust training and learning, enhancing their performance and capabilities.

Below are the questions concentrating on the integration of virtual machines and Ansible in the AI model build process.

Question 1:

How does utilizing virtual machines in AI model building enhance the development process?

a) Only for data visualization

*b) By providing isolated and replicable environments for consistent model development and testing

c) Only for improving data security

d) By reducing the need for data preprocessing

Explanation:

Virtual machines furnish isolated and replicable environments, enhancing the consistency and reliability of AI model development and testing. This ensures uniformity in development environments, contributing to efficient and reliable model building.

Question 2:

What is the role of Ansible in automating the AI model building process?

a) Only for handling data encryption

*b) It automates the configuration and deployment processes, ensuring consistent and efficient setup of development environments

c) Only for front-end development

d) It handles only the data visualization

Explanation:

Ansible plays a critical role in automating the configuration and deployment processes in AI model building. It ensures a consistent and efficient setup of development environments, minimizing manual errors and enhancing development speed and reliability.

Question 3:

How do virtual machines contribute to scalable AI model development?

a) Only by reducing computation time

*b) By allowing scalable and flexible resource allocation for model development and testing

c) Only by improving user interface

d) Only by handling data cleaning

Explanation:

Virtual machines contribute to the scalability of AI model development by allowing scalable and flexible resource allocation. This adaptability ensures that AI models can be developed and tested with varying resource allocations, enhancing the efficiency and flexibility of the model building process.

Question 4:

Why is Ansible's automation crucial for effective AI model building on virtual machines?

a) Only for enhancing graphical representation

*b) It ensures consistent and error-free configuration and deployment on virtual environments, enhancing the efficiency and reliability of AI model building

c) Only for data deletion

d) Only for data storage

Explanation:

Ansible’s automation is pivotal for AI model building on virtual machines as it ensures consistent and error-free configuration and deployment on virtual environments. This automation enhances the efficiency and reliability of AI model building by minimizing manual intervention and errors.

Question 5:

How does the combination of virtual machines and Ansible facilitate effective AI model development?

a) Only for improving security

*b) By ensuring scalable, consistent, and automated setup and deployment for AI model building

c) Only for data visualization

d) Only for reducing model size

Explanation:

The amalgamation of virtual machines and Ansible facilitates effective AI model development by ensuring scalable, consistent, and automated setup and deployment. This combination guarantees a streamlined and reliable model building process, aiding in the development of robust AI models.

Question 6:

What is a significant benefit of using virtual machines and Ansible in tandem for AI model building?

a) Only for enhancing model evaluation

*b) Enhanced scalability, automation, and consistency in the AI model building process

c) Only for improving data encryption

d) Only for front-end development

Explanation:

Employing virtual machines and Ansible in tandem for AI model building offers enhanced scalability, automation, and consistency in the AI model building process. This dual integration optimizes the overall development process, contributing to the building of robust and efficient AI models.

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