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Microsoft GH-300 Realistic Frenquent Update Free PDF

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Microsoft GH-300 Exam Syllabus Topics:

Topic
Details

Topic 1

Responsible :This section of the exam measures skills of AI Ethics Officers and Risk Managers and covers the responsible and ethical usage of AI technologies. It explains the risks and limitations associated with generative AI tools, including biases in training data and the need to validate AI outputs. Candidates learn how to operate AI responsibly by identifying potential harms such as bias, fairness, privacy concerns, and mitigating these harms by applying ethical AI principles.

Topic 2

Developer Use Cases for AI: Targeting Software Engineers and Technical Leads, this domain elaborates on how AI improves developer productivity across common tasks like learning new languages, translation, documentation, debugging, data science, and refactoring. It discusses Copilot’s support in software development lifecycle management and highlights its limitations. Use of the productivity API to track Copilot’s impact is also included.

Topic 3

Domain 4: Prompt Crafting and Prompt Engineering This section measures skills of Software Developers and AI Interaction Designers in effectively crafting prompts to optimize Copilot’s output. It reviews foundational concepts such as prompt components, the role of language in prompting, zero-shot vs. few-shot prompting, and how chat history influences responses. Best practices and engineering principles for prompt design and training methods are also covered.

Topic 4

Privacy Fundamentals and Context Exclusions: This domain focuses on Security Engineers and Compliance Officers and addresses improving code quality with Copilot’s test suggestions and security optimizations. It covers identification of security vulnerabilities, performance enhancements, and privacy features like content exclusions at repository and organization levels with explanation of their limitations. Candidates learn about safeguarding mechanisms such as duplication detection, contractual protections, security checks, and troubleshooting guide for common Copilot issues including context exclusions and suggestion gaps.

Topic 5

GitHub Copilot Plans and Feature: This domain targets Product Managers and DevOps Engineers and focuses on understanding the various GitHub Copilot subscription plans like Individual, Business, and Enterprise, including distinctions and management features. It covers how Copilot is integrated into IDEs, different triggering methods for code suggestions, organizational policy management, subscription administration via API, and effective use of Copilot Chat and Knowledge Bases. Candidates also learn about CLI usage and configuration.

Topic 6

How GitHub Copilot Works and Handles Data: Designed for Machine Learning Engineers and Data Privacy Specialists, this section covers the data lifecycle and processing behind Copilot’s code suggestions. It explains how context is gathered, prompts constructed, responses generated, and post-processed through proxy services. Candidates understand Copilot’s data policies, handling of inputs, and limitations such as context window size and data age influencing suggestion relevance.

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Microsoft GitHub Copilot Sample Questions (Q100-Q105):

NEW QUESTION # 100
effectiveness of GitHub Copilot? (Each correct answer presents part of the solution. Choose two.) 1
1.
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A. The API can provide feedback on coding style and standards compliance.
B. The API can track the acceptance rate of code suggestions accepted and used in the organization.
C. The API can provide Copilot Chat specific suggestions acceptance metrics.
D. The API can generate detailed reports on code quality improvements made by GitHub Copilot.
E. The API can refactor your code to improve productivity.

Answer: B,C

Explanation:
The GitHub Copilot usage metrics API provides insights into the acceptance rate of code suggestions and Copilot Chat specific suggestions acceptance metrics, helping organizations evaluate its effectiveness.

NEW QUESTION # 101
What practices enhance the quality of suggestions provided by GitHub Copilot? (Select three.)

A. Providing examples of desired output
B. Including personal information in the code comments
C. Clearly defining the problem or task
D. Using meaningful variable names
E. Use a .gitignore file to exclude irrelevant files

Answer: A,C,D

Explanation:
The quality of Copilot's suggestions is enhanced by clearly defining the task, using meaningful variable names, and providing examples of the desired output.

NEW QUESTION # 102
How does GitHub Copilot typically handle code suggestions that involve deprecated features or syntax of programming languages?

A. GitHub Copilot always filters out deprecated elements to promote the use of current standards.
B. GitHub Copilot rejects all prompts involving deprecated features to avoid compilation errors.
C. GitHub Copilot may suggest deprecated syntax or features if they are present in its training data.
D. GitHub Copilot automatically updates deprecated features in its suggestions to the latest version.

Answer: C

Explanation:
"GitHub Copilot may sometimes suggest deprecated code, APIs, or patterns if these appear in its training data. Users are responsible for reviewing and updating the suggestions." This confirms that Copilot does not automatically update or reject deprecated features, but may still suggest them if they were part of training.
References: GitHub Copilot usage limitations documentation.

NEW QUESTION # 103
How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

A. By opening the relevant tabs in your IDE
B. By adding the important files to your .gitconfig
C. By adding relevant code snippets to your prompt
D. By adding the full file paths to your prompt of important files

Answer: A,C

Explanation:
Improving the context for GitHub Copilot involves opening relevant files in your IDE to provide immediate context and adding relevant code snippets directly to your prompts to give Copilot specific examples and information.

NEW QUESTION # 104
Which Microsoft ethical AI principle is aimed at ensuring AI systems treat all people equally?

A. Inclusiveness
B. Fairness
C. Reliability and Safety
D. Privacy and Security

Answer: B

Explanation:
The principle of fairness ensures that AI systems treat all people equally and avoid discriminatory outcomes.

NEW QUESTION # 105
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