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Microsoft GH-300 Exam Overview:
| Certification Vendor: | Microsoft |
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| Exam Name: | GitHub Copilot |
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| Exam Number: | GH-300 |
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| Exam Format: | Multiple-choice, Scenario-based |
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| Available Languages: | Korean, Portuguese (Brazil), Spanish, Japanese, English |
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| Certificate Validity Period: | 1 year (renewable) |
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| Exam Duration: | 100 minutes |
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| Related Certifications: | GitHub Foundations
GitHub Actions |
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| Real Exam Qty: | 65 |
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| Exam Price: | $99 USD |
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| Passing Score: | 700 / 1000 |
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| Sample Questions: | Microsoft GH-300 Sample Questions |
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| Exam Way: | Online proctored exam (Pearson VUE) |
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| Pre Condition: | No formal prerequisites. Microsoft recommends practical experience using GitHub Copilot in an IDE and a general understanding of software development principles. |
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| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/github-copilot |
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| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - 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.
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| Topic 3 | - 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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| Topic 4 | - 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.
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| 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 (Q59-Q64):
NEW QUESTION # 59
Why is it important to ensure the security of the code used in Generative AI (Gen AI) tools?
- A. Ensuring code security maintains the integrity of the AI system.
- B. Ensuring code security enables the AI system to handle larger datasets effectively.
- C. Ensuring code security supports the development of more advanced AI features.
- D. Ensuring code security prevents unauthorized access and potential data breaches.
Answer: D
Explanation:
"Securing the code used with generative AI prevents unauthorized access, helps safeguard sensitive data, and protects against potential breaches." This confirms option A is correct because the primary importance lies in avoiding unauthorized access and breaches.
References: GitHub Copilot responsible use and AI security documentation.
NEW QUESTION # 60
Why might a Generative AI (Gen AI) tool create inaccurate outputs?
- A. The Gen AI tool is overloaded with too many requests at once.
- B. The Gen AI tool is experiencing downtime and is not fully recovered.
- C. The training data might contain biases or inconsistencies.
- D. The Gen AI tool is programmed with a focus on creativity over factual accuracy.
Answer: C
Explanation:
Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.
NEW QUESTION # 61
What GitHub Copilot configuration needs to be enabled to protect against IP infringements?
- A. Blocking public code matches
- B. Allowing license check configuration
- C. Blocking license check configuration
- D. Allowing public code matches
Answer: A
Explanation:
"By default, suggestions matching public code are allowed, but you can choose to block public code matches.
This setting can be enabled to reduce the risk of intellectual property infringement." This shows that enabling "Blocking public code matches" is the configuration required to protect against IP issues.
References: GitHub Copilot configuration settings documentation.
NEW QUESTION # 62
Which of the following is a risk associated with using AI?
- A. AI algorithms are incapable of perpetuating existing biases.
- B. AI eliminates the need for data privacy regulations.
- C. AI systems can sometimes make decisions that are difficult to interpret.
- D. AI replaces the need for developer opportunities in most fields.
Answer: C
Explanation:
A risk associated with AI is that its decisions can be difficult to interpret, leading to a lack of transparency and potential misunderstandings.
NEW QUESTION # 63
How long does it take content exclusion to add or be updated?
- A. 45 - 60 minutes
- B. Up to 30 minutes
- C. 60 - 90 minutes
- D. 24 hours
Answer: B
Explanation:
Content exclusions typically take up to 30 minutes to be added or updated.
Reference: GitHub Copilot content exclusion documentation.
NEW QUESTION # 64
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