Pass the ECCouncil ECDE 312-97 Questions and answers with CertsForce

Interactive Application Security Testing (IAST) works by instrumenting the application at runtime, allowing it to observe both thesource code execution pathsand theHTTP requests and responsesflowing through the application. Because of this dual visibility, IAST can analyze every executed line of code (similar to SAST) while also monitoring real-time application behavior (similar to DAST). This unique capability enables highly accurate vulnerability detection with fewer false positives. The other options do not correctly explain how IAST achieves this hybrid analysis. Access to both code and HTTP traffic is what allows IAST to bridge static and dynamic testing techniques, making it highly effective across development, QA, and production environments.

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Change lead time measures the duration between a code commit and its successful deployment into production. This metric tracks how efficiently new features, bug fixes, and changes move through development, testing, and release stages. It is a key DevSecOps performance indicator used to assess pipeline efficiency and the effectiveness of automation and security integration. Mean time to recovery focuses on restoring service after incidents, change volume measures the number of changes rather than delivery speed, and time to value is a broader business metric. Change lead time directly reflects how well DevSecOps practices enable rapid yet secure delivery, making it the correct metric for measuring commit-to-production flow across all phases.

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Bandit is a Static Application Security Testing (SAST) tool developed specifically for analyzingPython source code. It scans Python scripts and applications to identify common security issues such as use of weak cryptography, hardcoded passwords, unsafe use of functions like eval, and insecure imports. Bandit works by parsing Python Abstract Syntax Trees (ASTs) and applying a set of security-focused rules. It does not support Java, Ruby, or C++ code, which require different static analysis tools tailored to their respective languages. By integrating Bandit with Jenkins during the Build and Test stage, Allen enables automated detection of Python-specific security flaws as soon as code changes are introduced. This shift-left approach reduces remediation costs, prevents vulnerable code from progressing further in the pipeline, and improves overall application security posture.

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The practices described—user identification and authorization, auditing, defense-in-depth, data protection, integrity enforcement, and input validation—are core elements that are planned and architected into the system from the beginning. These controls reflectSecure by Design, which focuses on embedding security principles at the design and architecture stage rather than adding them later. Secure by implementation emphasizes coding correctness, secure by default focuses on default configurations, and secure by communication focuses on trusted communication channels. DWART’s approach shows a holistic security mindset that anticipates attacker behavior and integrates layered defenses and controls into the system blueprint. This aligns directly with Secure by Design, which aims to reduce systemic risk by ensuring the application’s foundational structure enforces security consistently across all components and use cases.

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In Conjur secret management, variables are first declared in policy files and then populated with actual secret values using the Conjur CLI. The correct command to assign a value to a variable is conjur variable set, where the -i option specifies the fully qualifiedpolicy path of the variable name, and the -v option specifies thesecret valueto be stored securely. This command writes the secret into Conjur’s encrypted vault and associates it with the declared variable so that Jenkins jobs can retrieve it securely at runtime. The other options misuse flags or reverse their meanings, which would result in invalid commands or incorrect secret handling. Integrating Conjur with Jenkins during the Build and Test stage ensures that sensitive credentials such as passwords, API keys, and tokens are never hard-coded in pipeline scripts or source code. Instead, secrets are dynamically fetched when required, supporting least-privilege access, auditability, and compliance requirements—critical for financial-sector applications.

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Conjur policies define access controls, authentication rules, and secret variables, and they must be loaded into the correct policy branch. The conjur policy load command uses the -b flag to specify thepolicy branchand the -f flag to specify thepolicy file. To load a policy into the root branch, the correct command is conjur policy load -b root -f . Options that reverse or misuse these flags are invalid and would either fail or load the policy incorrectly. Loading policies correctly during the Build and Test stage ensures that Jenkins pipelines can securely access secrets at runtime, enforcing centralized secret management, least-privilege access, and compliance with security requirements.

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GitHub Code Scanning is a built-in security capability designed to automatically analyze source code for security vulnerabilities and coding errors. The primary and officially supported engine for GitHub Code Scanning isCodeQL. CodeQL works by converting source code into a database and running security queries to detect issues such as injection flaws, insecure coding patterns, and logic errors. William can enable CodeQL by configuring GitHub Actions with either the default or advanced CodeQL workflow. Once enabled, CodeQL scans are triggered on events such as code pushes and pull requests, and the results appear as code scanning alerts in the repository’s Security tab. Gauntlt is a security testing harness used mainly for infrastructure and application testing, GitMiner is used to discover sensitive data like secrets in repositories, and OWASP ZAP is a dynamic application security testing tool used against running applications. None of these tools configure GitHub’s native Code Scanning feature. Therefore, CodeQL is the correct tool to set up GitHub Code Scanning in the Code stage of a DevSecOps pipeline.

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Bundler-Audit is an SCA tool designed specifically for Ruby applications. It analyzes the Gemfile and Gemfile.lock to identify dependencies and checks them against known vulnerability databases. Bandit is intended for Python code analysis, Retire.js targets JavaScript libraries, and Tenable.io focuses on infrastructure-level vulnerabilities. By using Bundler-Audit during the Code stage, DevSecOps teams can detect vulnerable Ruby gems early and ensure that only secure dependencies are used. This reduces the risk of exploiting known vulnerabilities in third-party libraries and supports secure dependency management throughout the development lifecycle.

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Azure resource groups are created using the Azure CLI command az group create. The --name parameter specifies the resource group name, and --location defines the Azure region. Option A uses the correct CLI prefix (az), command group (group create), and valid parameters. Options B, C, and D are incorrect due to invalid command abbreviations or incorrect CLI prefixes (azure instead of az). Creating a resource group is a foundational step in the Release and Deploy stage, as it provides a logical container for AKS clusters, networking components, and related resources, enabling organized, secure, and manageable deployments.

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Build-time checks are designed to enforce security gates within the CI/CD pipeline. When critical vulnerabilities such as SQL injection and cross-site scripting (XSS) are detected during this stage, the correct and expected behavior is tofail the build. Stopping the build process prevents insecure code from progressing to later stages such as testing, deployment, or production. Ignoring issues or merely sending alerts while continuing the pipeline undermines the purpose of shift-left security. Alerts to SIEM systems and issue trackers are typically supplementary actions, but the primary enforcement mechanism at build time is to block the pipeline when severity thresholds are exceeded. This approach reduces remediation costs, limits exposure, and ensures that only secure artifacts move forward in the DevSecOps lifecycle.

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