GH-500 Microsoft GitHub Advanced Security Exam Free Practice Exam Questions (2026 Updated)

Comprehensive and Detailed Explanation:

Secret scanning push protection is a proactive feature that scans for secrets in your code during the push process. If a secret is detected, the push is blocked, preventing the secret from being added to the repository. This helps prevent accidental exposure of sensitive information.​

GitHub Docs

Comprehensive and Detailed Explanation:

Before defining a custom pattern for secret scanning in a repository, you must enable secret scanning for that repository. Secret scanning must be active to utilize custom patterns, which allow you to define specific formats (using regular expressions) for secrets unique to your organization.​

Once secret scanning is enabled, you can add custom patterns to detect and prevent the exposure of sensitive information tailored to your needs.​

A Dependabot alert is only marked as resolved when the related vulnerability is no longer present in your code — specifically after you merge a pull request that updates the vulnerable dependency.

Simply viewing alerts or graphs does not affect their status. Ignoring the alert by leaving the repo unchanged keeps the vulnerability active and unresolved.

Secret scanning for a private repository is enabled through the repository's security configuration/settings, currently located under Settings → Security and quality → Advanced Security. In current GitHub terminology, enabling Secret Protection enables secret scanning alerts for an eligible private or internal repository. Repository rulesets control policies such as branch protection, required checks, and merge conditions; they are not the primary mechanism for enabling secret scanning. GitHub also does not use a secret.yml file to activate the feature. Code scanning default setup configures CodeQL analysis and is separate from secret detection. Organization owners can additionally enable Secret Protection and secret-scanning capabilities at scale by applying security configurations to repositories. Consequently, the code security or Advanced Security settings area is the correct location, making A the correct answer.

Comprehensive and Detailed Explanation:

In the advanced setup for CodeQL code scanning, GitHub generates a workflow file named codeql-analysis.yml. This file is located in the .github/workflows directory of your repository. It defines the configuration for the CodeQL analysis, including the languages to analyze, the events that trigger the analysis, and the steps to perform during the workflow.​

To ensure you're notified whenever a vulnerability is detected via Dependabot, you must enable alerts for Dependabot in your personal notification settings. This applies to both new and existing repositories. It ensures you get timely alerts about security vulnerabilities.

The dependency graph must be enabled for scanning, but does not send alerts itself.

When a CodeQL workflow uses a language matrix, the matrix explicitly defines which programming languages the workflow analyzes. GitHub states that CodeQL will analyze only the languages included in that matrix; adding or removing matrix entries therefore directly changes the languages scanned. The matrix also allows separate language-analysis jobs to run in parallel, so it does not inherently make analysis sequential. The languages input of the CodeQL init action commonly receives the current matrix.language value, but that describes how the matrix value is passed into CodeQL rather than the principal impact asked for here. The matrix also does not represent dependency exclusions. Consequently, the operational effect being tested is that CodeQL analysis is restricted to the languages explicitly included in the workflow's matrix.

Comprehensive and Detailed Explanation:

In private repositories, users with write access can fix code scanning alerts. They can do this by committing changes that address the issues identified by the code scanning tools. This level of access ensures that only trusted contributors can modify the code to resolve potential security vulnerabilities.​

GitHub Docs

Users with read or triage roles do not have the necessary permissions to make code changes, and the security manager role is primarily focused on managing security settings rather than directly modifying code.​

Dependabot alert and advisory details can include a CVE identifier, a GHSA identifier, and associated CWE classifications. CVE provides the standardized identifier for a publicly known vulnerability. GitHub assigns a unique GHSA ID to advisories in the GitHub Advisory Database, while CWE identifies the underlying category or class of software weakness associated with the vulnerability. GitHub's Dependabot alert interface exposes these values when they are available. EPSS is also now surfaced by GitHub and can be used to prioritize alerts, but EPSS is a probability-based exploit-risk score, not one of the three vulnerability identifiers/classifications being tested by this question. SAST describes a security-analysis methodology rather than an advisory classification. Therefore, the intended selections are CVE, GHSA, and CWE: C, D, and E.

Comprehensive and Detailed Explanation:

To generate a dependency graph for a private repository, GitHub requires:​

Dependency graph enabled: The repository must have the dependency graph feature enabled. This can be configured at the organization level to apply to all new private repositories.​

Access to manifest and lock files: GitHub needs read-only access to the repository's dependency manifest and lock files (e.g., package.json, requirements.txt) to identify and map dependencies.​

If a secret is intentionally used in a test environment and poses no real-world security risk , you may close the alert with the reason "used in tests" . This helps reduce noise and clarify that the alert was reviewed and accepted as non-critical.

Just being in a test file isn't enough unless its purpose is purely for testing .

Enabling the dependency graph allows GitHub to analyze supported manifest and lock files and build a representation of the repository's direct and transitive dependencies. GitHub receives read-only access to the dependency files, parses supported manifests when the feature is first enabled, and then updates the graph as repository content changes. Dependabot alerts and Dependabot security updates are separate capabilities: enabling the dependency graph alone does not automatically mean that security-update pull requests will be generated. Likewise, the dependency graph setting for one repository does not require administrators to manually enable dependency information for every newly created repository unless their organization's configuration requires that behavior. Therefore, the applicable result among the supplied options is that repository administrators can view the dependency information exposed by the dependency graph.

In a repository's Security tab, you can view:

Secret scanning alerts : Exposed credentials or tokens

Dependabot alerts : Vulnerable dependencies from the advisory database

Code scanning alerts : Vulnerabilities in code detected via static analysis (e.g., CodeQL)

You won’t see general "security status alerts" (not a formal category) or permission-related alerts here.

Code scanning merge requirements and severity thresholds are repository configuration controls and are therefore managed from the repository's Settings area. In GitHub's current interface, administrators can create a branch ruleset under Settings and configure Require code scanning results. They can then select the code scanning tool and specify alert thresholds, including which normal alerts or security-severity levels should prevent merging. The Security and quality tab is primarily where security findings are reviewed and triaged, while the Actions tab displays workflow execution information. The Pull Requests tab is used to review and merge proposed changes rather than configure repository-wide code scanning policy. Thus, although GitHub's implementation has evolved toward ruleset-based merge protection, the appropriate top-level location remains the repository's Settings tab.

When using a SARIF-compatible tool within GitHub Actions, it's necessary to explicitly add a step in your workflow to upload the analysis results. This is typically done using the upload-sarif action, which takes the SARIF file generated by your tool and uploads it to GitHub for processing and display in the Security tab. Without this step, the results won't be available in GitHub's code scanning interface.​

Comprehensive and Detailed Explanation:

Dependency review is triggered by specific events in GitHub workflows:​

pull_request: When a pull request is opened, synchronized, or reopened, GitHub can analyze the changes in dependencies and provide a dependency review.​

workflow_dispatch: This manual trigger allows users to initiate workflows, including those that perform dependency reviews.​

The trigger and commit options are not recognized GitHub Actions events and would not initiate a dependency review.​

In a dependabot.yml configuration file, package-ecosystem is a required key . It defines the package manager being used in that update configuration (e.g., npm, pip, maven, etc.).

Without this key, Dependabot cannot determine how to analyze or update dependencies. Other keys like rebase-strategy or commit-message are optional and used for customizing behavior.

Dependabot alerts utilize standardized identifiers to describe vulnerabilities:​

CVE (Common Vulnerabilities and Exposures): A widely recognized identifier for publicly known cybersecurity vulnerabilities.​

CWE (Common Weakness Enumeration): A category system for software weaknesses and vulnerabilities.​

These identifiers help developers understand the nature of the vulnerabilities and facilitate the search for more information or remediation strategies.​

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.