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[Hardware] Current GH-300 Exam Content & Latest GH-300 Exam Questions

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【Hardware】 Current GH-300 Exam Content & Latest GH-300 Exam Questions

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Microsoft GH-300 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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 5
  • Domain 6: Testing with GitHub Copilot: This section measures abilities of QA Engineers and Test Automation Specialists to use Copilot for test generation, including unit and integration tests. It explains how Copilot can identify edge cases and assist in writing assertions. The domain also covers different Copilot subscription SKUs, privacy considerations, organizational code suggestion settings, and configuration files related to Copilot.

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Microsoft GitHub Copilot Sample Questions (Q26-Q31):NEW QUESTION # 26
What caution should developers exercise when using GitHub Copilot for assistance with mathematical computations?
  • A. GitHub Copilot's ability to execute and verify mathematical results in real-time.
  • B. GitHub Copilot's capability to optimize complex mathematical algorithms beyond manual coding.
  • C. GitHub Copilot's automatic update of outdated mathematical formulas to modern standards.
  • D. GitHub Copilot's reliance on pattern-based responses without verifying computation accuracy.
Answer: D
Explanation:
"Copilot's responses are generated based on patterns in training data and are not guaranteed to be mathematically accurate. Developers must verify calculations independently." This shows that Copilot cannot guarantee correctness in math and relies on patterns, making option C correct.
References: GitHub Copilot responsible use documentation.

NEW QUESTION # 27
A company is currently storing code in Bitbucket and would like to use GitHub Copilot. Which GitHub Copilot plan will be most cost effective to allow them to manage users with their Identity Provider (e.g. Okta)?
  • A. GitHub Copilot Business for non-GHE customers
  • B. GitHub Copilot Individual
  • C. GitHub Copilot Enterprise
  • D. GitHub Copilot Teams
Answer: C
Explanation:
GitHub Copilot Enterprise is the most cost-effective plan for managing users with an Identity Provider like Okta, as it provides enterprise-level features and integration.

NEW QUESTION # 28
How do you generate code suggestions with GitHub Copilot in the CLI?
  • A. Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.
  • B. Type out the code snippet -> Use the copilot refine command to enhance it -> Review the suggested command.
  • C. Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.
  • D. Use copilot suggest -> Write the command you want -> Select the best suggestion from the list.
Answer: C
Explanation:
In the CLI, GitHub Copilot generates code suggestions by analyzing code comments. You write comments describing what you want, and Copilot provides relevant code suggestions. You then select the best suggestion from the list.
Reference: GitHub Copilot CLI documentation.

NEW QUESTION # 29
GitHub Copilot in the Command Line Interface (CLI) can be used to configure the following settings: (Each correct answer presents part of the solution. Choose two.)
  • A. Usage analytics
  • B. The default execution confirmation
  • C. The default editor
  • D. GitHub CLI subcommands
Answer: A,B
Explanation:
GitHub Copilot in the CLI allows configuration of settings such as the default execution confirmation and usage analytics. These settings help tailor the CLI experience to the user's preferences.
Reference: GitHub Copilot CLI configuration documentation.

NEW QUESTION # 30
What types of prompts or code snippets might be flagged by the GitHub Copilot toxicity filter? (Each correct answer presents part of the solution. Choose two.)
  • A. Sexually suggestive or explicit content
  • B. Code that contains logical errors or produces unexpected results
  • C. Code comments containing strong opinions or criticisms
  • D. Hate speech or discriminatory language (e.g., racial slurs, offensive stereotypes)
Answer: A,D
Explanation:
GitHub Copilot includes a toxicity filter to prevent the generation of harmful or inappropriate content. This filter flags prompts or code snippets that contain hate speech, discriminatory language, or sexually suggestive or explicit content. This ensures a safe and respectful coding environment.
Reference: GitHub Copilot documentation on safety and content filtering.

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