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| Section | Weight | Objectives |
|---|---|---|
| Configure and use Code Security | 10–15% | - Configure workflows, templates, and scan frequency - Set up and enable code scanning - Integrate with GitHub Actions or external CI systems - Analyze, triage, and remediate scan results |
| Security operations: best practices, prioritization, and remediation | 15–20% | - Prioritize risks based on severity, impact, and context - Apply remediation workflows and automation - Document and track security improvements - Detect, manage, and respond to security alerts |
| Describe GitHub Security suites, features, and ecosystem | 15–20% | - Explain security campaigns and risk reduction - Understand GitHub Security suites and architecture - Compare prevention-first vs gate-based security strategies - Describe suite structure, navigation, and end-to-end secure SDLC |
| GitHub Security suites administration | 10–15% | - Manage access and licensing for GHAS - Configure organization and repository policies - Plan deployment and rollout strategies - Monitor usage, compliance, and audit logs |
| Configure and use supply chain security | 15–20% | - Configure Dependency Review and enforce policies - Enable and configure Dependabot and dependency updates - Analyze dependency risks and vulnerabilities - Manage license compliance and security policies |
| Configure and use Secret Protection | 15–20% | - Configure settings and feature availability - Contrast behavior between public and private/enterprise repositories - Enable and configure at repository and organization levels - Prevent secret exposure and manage push protection |
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NEW QUESTION # 87
What is required to trigger code scanning on a specified branch?
Answer: A
NEW QUESTION # 88
How would you build your code within the CodeQL analysis workflow? (Each answer presents a complete solution. Choose two.)
Answer: B,E
Explanation:
Comprehensive and Detailed Explanation:
When setting up CodeQL analysis for compiled languages, there are two primary methods to build your code:
GitHub Docs
Autobuild: CodeQL attempts to automatically build your codebase using the most likely build method. This is suitable for standard build processes.
GitHub Docs
Custom Build Steps: For complex or non-standard build processes, you can implement custom build steps by specifying explicit build commands in your workflow. This provides greater control over the build process.
GitHub Docs
The init action initializes the CodeQL analysis but does not build the code. The jobs.analyze.runs-on specifies the operating system for the runner but is not directly related to building the code. Uploading compiled binaries is not a method supported by CodeQL for analysis.
References: GitHub Docs - CodeQL code scanning for compiled languages
NEW QUESTION # 89
If default code security settings have not been changed at the repository, organization, or enterprise level, which repositories receive Dependabot alerts?
Answer: A
Explanation:
By default , no repositories receive Dependabot alerts unless configuration is explicitly enabled. GitHub does not enable Dependabot alerts automatically for any repositories unless:
* The feature is turned on manually
* It's configured at the organization or enterprise level via security policies This includes public, private, and enterprise-owned repositories - manual activation is required .
: GitHub Docs - About Dependabot Alerts
NEW QUESTION # 90
What does a CodeQL database of your repository contain?
Answer: C
Explanation:
GitHub
Agentic AI for AppSec Teams
Explanation:
Comprehensive and Detailed Explanation:
A CodeQL database contains a representation of your codebase, including the build of the code and extracted data. This database is used to run CodeQL queries to analyze your code for potential vulnerabilities and errors.
GitHub Docs
NEW QUESTION # 91
When using CodeQL, how does extraction for compiled languages work?
Answer: D
Explanation:
For compiled languages, CodeQL performs extraction by monitoring the normal build process. This means it watches your usual build commands (like make, javac, or dotnet build) and extracts the relevant data from the actual build steps being executed. CodeQL uses this information to construct a semantic database of the application.
This approach ensures that CodeQL captures a precise, real-world representation of the code and its behavior as it is compiled, including platform-specific configurations or conditional logic used during build.
NEW QUESTION # 92
......
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