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Microsoft GH-500 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Configure and use secret scanning: This domain targets DevOps Engineers and Security Analysts with the skills to configure and manage secret scanning. It includes understanding what secret scanning is and its push protection capability to prevent secret leaks. Candidates differentiate secret scanning availability in public versus private repositories, enable scanning in private repos, and learn how to respond appropriately to alerts. The domain covers alert generation criteria for secrets, user role-based alert visibility and notification, customizing default scanning behavior, assigning alert recipients beyond admins, excluding files from scans, and enabling custom secret scanning within repositories.
Topic 2
  • Configure and use Code Scanning with CodeQL: This domain measures skills of Application Security Analysts and DevSecOps Engineers in code scanning using both CodeQL and third-party tools. It covers enabling code scanning, the role of code scanning in the development lifecycle, differences between enabling CodeQL versus third-party analysis, implementing CodeQL in GitHub Actions workflows versus other CI tools, uploading SARIF results, configuring workflow frequency and triggering events, editing workflow templates for active repositories, viewing CodeQL scan results, troubleshooting workflow failures and customizing configurations, analyzing data flows through code, interpreting code scanning alerts with linked documentation, deciding when to dismiss alerts, understanding CodeQL limitations related to compilation and language support, and defining SARIF categories.
Topic 3
  • Configure and use Dependabot and Dependency Review: Focused on Software Engineers and Vulnerability Management Specialists, this section describes tools for managing vulnerabilities in dependencies. Candidates learn about the dependency graph and how it is generated, the concept and format of the Software Bill of Materials (SBOM), definitions of dependency vulnerabilities, Dependabot alerts and security updates, and Dependency Review functionality. It covers how alerts are generated based on the dependency graph and GitHub Advisory Database, differences between Dependabot and Dependency Review, enabling and configuring these tools in private repositories and organizations, default alert settings, required permissions, creating Dependabot configuration files and rules to auto-dismiss alerts, setting up Dependency Review workflows including license checks and severity thresholds, configuring notifications, identifying vulnerabilities from alerts and pull requests, enabling security updates, and taking remediation actions including testing and merging pull requests.
Topic 4
  • Describe the GHAS security features and functionality: This section of the exam measures skills of Security Engineers and Software Developers and covers understanding the role of GitHub Advanced Security (GHAS) features within the overall security ecosystem. Candidates learn to differentiate security features available automatically for open source projects versus those unlocked when GHAS is paired with GitHub Enterprise Cloud (GHEC) or GitHub Enterprise Server (GHES). The domain includes knowledge of Security Overview dashboards, the distinctions between secret scanning and code scanning, and how secret scanning, code scanning, and Dependabot work together to secure the software development lifecycle. It also covers scenarios contrasting isolated security reviews with integrated security throughout the development lifecycle, how vulnerable dependencies are detected using manifests and vulnerability databases, appropriate responses to alerts, the risks of ignoring alerts, developer responsibilities for alerts, access management for viewing alerts, and the placement of Dependabot alerts in the development process.
Topic 5
  • Describe GitHub Advanced Security best practices, results, and how to take corrective measures: This section evaluates skills of Security Managers and Development Team Leads in effectively handling GHAS results and applying best practices. It includes using Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) identifiers to describe alerts and suggest remediation, decision-making processes for closing or dismissing alerts including documentation and data-based decisions, understanding default CodeQL query suites, how CodeQL analyzes compiled versus interpreted languages, the roles and responsibilities of development and security teams in workflows, adjusting severity thresholds for code scanning pull request status checks, prioritizing secret scanning remediation with filters, enforcing CodeQL and Dependency Review workflows via repository rulesets, and configuring code scanning, secret scanning, and dependency analysis to detect and remediate vulnerabilities earlier in the development lifecycle, such as during pull requests or by enabling push protection.

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Microsoft GitHub Advanced Security Sample Questions (Q58-Q63):

NEW QUESTION # 58
Which patterns are secret scanning validity checks available to?

Answer: A

Explanation:
Validity checks - where GitHub verifies if a secret is still active - are available for partner patterns only.
These are secrets issued by GitHub's trusted partners (like AWS, Slack, etc.) and have APIs for GitHub to validate token activity status.
Custom patterns and high entropy patterns do not support automated validity checks.
: GitHub Docs - Secret Validation for Partner Patterns


NEW QUESTION # 59
Which syntax in a query suite tells CodeQL to look for one or more specified .ql files?

Answer: A

Explanation:
In a query suite (a .qls file), the **query** key is used to specify the paths to one or more .ql files that should be included in the suite.
Example:
- query: path/to/query.ql
* qls is the file format.
* qlpack is used for packaging queries, not in suite syntax.
: GitHub Docs - CodeQL Query Suite Syntax


NEW QUESTION # 60
When using CodeQL, how does extraction for compiled languages work?

Answer: D

Explanation:
For compiled languages, CodeQL performs extraction bymonitoring 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 # 61
You are configuring a CodeQL workflow for compiled languages. What happens if your workflow uses a language matrix?

Answer: D

Explanation:
If your workflow uses a language matrix, autobuild attempts to build each of the compiled languages listed in the matrix. Without a matrix autobuild attempts to build the supported compiled language that has the most source files in the repository. With the exception of Go, analysis of other compiled languages in your repository will fail unless you supply explicit build commands.
Note:
CodeQL build modes
The CodeQL action supports three different build modes for compiled languages:
none - the CodeQL database is created directly from the codebase without building the codebase (supported for all interpreted languages, and additionally supported for C/C++, C# and Java).
autobuild - CodeQL detects the most likely build method and uses this to attempt to build the codebase and create a database for analysis (supported for all compiled languages).
manual - you define the build steps to use for the codebase in the workflow (supported for all compiled languages, except Rust).


NEW QUESTION # 62
What does a CodeQL database of your repository contain?

Answer: A

Explanation:
CodeQL databases contain queryable data extracted from a codebase, for a single language at a particular point in time. The database contains a full, hierarchical representation of the code, including a representation of the abstract syntax tree, the data flow graph, and the control flow graph.
Each language has its own unique database schema that defines the relations used to create a database. The schema provides an interface between the initial lexical analysis during the extraction process, and the actual complex analysis using CodeQL. The schema specifies, for instance, that there is a table for every language construct.
For each language, the CodeQL libraries define classes to provide a layer of abstraction over the database tables. This provides an object-oriented view of the data which makes it easier to write queries.


NEW QUESTION # 63
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