GH-300 Certified Questions | GH-300 Valid Exam Experience

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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
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
  • 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 5
  • 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 (Q92-Q97):

NEW QUESTION # 92
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?

Answer: D

Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.
Reference: GitHub Copilot Business audit logs.


NEW QUESTION # 93
Which REST API endpoint is used to modify details about a GitHub Copilot Business subscription? (Each correct answer presents part of the solution. Choose two.)

Answer: C,E


NEW QUESTION # 94
What is few-shot prompting?

Answer: A

Explanation:
"Few-shot prompting is a technique where you provide examples of the mechanism or output you want the model to follow. These examples guide the AI's responses." This proves that option A is correct.
References: GitHub Copilot prompt engineering documentation.


NEW QUESTION # 95
What is the main purpose of the duplication detection filter in GitHub Copilot?

Answer: C

Explanation:
"GitHub Copilot has a duplication detection filter that checks suggestions of about 150 characters or longer against public code on GitHub. If a match is found, the suggestion is blocked if you have configured blocking." This clearly defines the purpose as preventing Copilot from suggesting verbatim code found publicly.
References: GitHub Copilot duplication detection documentation.


NEW QUESTION # 96
What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot?

Answer: D

Explanation:
When the public code filter is enabled, GitHub Copilot runs code suggestions through filters designed to detect matches with publicly available code. This helps prevent the generation of code that might infringe on copyright or licensing agreements.
Reference: GitHub Copilot documentation on public code filtering and licensing.


NEW QUESTION # 97
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