Free and Premium ECCouncil 312-97 Dumps Questions Answers

Runtime Application Self-Protection (RASP) operates from within the application runtime environment, monitoring incoming traffic, API calls, and execution behavior in real time. Because it has deep visibility into application logic and execution context, RASP can accurately detect attacks such as injection, tampering, and abnormal behavior while minimizing false positives. SAST analyzes source code statically, DAST tests running applications externally, and IAST combines some runtime insight with testing but does not actively block threats. RASP’s ability to detect and remediate attacks during runtime makes it ideal for protecting applications in production environments, aligning with the Operate and Monitor stage of the DevSecOps pipeline.

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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Retire.js is a Software Composition Analysis (SCA) tool designed specifically to identify known vulnerabilities in JavaScript libraries used in web and NodeJS applications. It scans dependencies and compares detected versions against a vulnerability database to identify insecure libraries. Bandit is a static analysis tool for Python, Bundler-Audit is used for Ruby dependencies, and Tenable.io focuses on infrastructure and vulnerability management rather than JavaScript libraries. Using Retire.js during the Code stage allows DevSecOps teams to identify insecure third-party dependencies early, reducing the likelihood of vulnerable libraries being deployed into production. This supports shift-left security and strengthens the application’s overall security posture.

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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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ThreatModeler integration with Azure Pipelines is achieved using abidirectional API, which allows automated and continuous interaction between the pipeline and the threat modeling platform. This bidirectional communication enables Azure Pipelines to trigger threat modeling activities while also receiving results, risk scores, and actionable insights back from ThreatModeler. Such feedback loops are critical for proactive security decision-making during the Plan stage of DevSecOps. Unidirectional APIs or UI-based integrations limit automation and do not support continuous feedback, making them unsuitable for pipeline-driven workflows. UI-based approaches also introduce manual steps, which conflict with DevSecOps principles of automation and consistency. By using a bidirectional API, William’s organization can embed threat modeling into the planning process, identify architectural risks early, and ensure security considerations are continuously enforced as part of the pipeline.

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Coverity is a well-known Static Application Security Testing (SAST) tool used to analyze source code for security vulnerabilities, coding errors, and quality issues. It integrates with CI/CD pipelines and supports enterprise-scale environments, including cloud-based development on platforms such as Azure. Accurics focuses on Infrastructure as Code security, Tenable.io is a vulnerability management platform for infrastructure and assets, and DevSkim is a lightweight code scanning extension rather than a full SAST platform. Selecting Coverity enables deep static analysis of application code during the Code stage, helping teams detect vulnerabilities early and reduce remediation costs.

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On Linux systems, Jenkins environment variables such as AWS access key ID, secret access key, and default region are commonly configured in the /etc/sysconfig/Jenkins file. This file allows administrators to define environment variables that are loaded when the Jenkins service starts. By placing AWS credentials and region information in this file, Jenkins jobs and plugins—such as the AWS Secrets Manager Credentials Provider—can securely access AWS resources. The other options reference invalid paths or unrelated configuration files (such as Filebeat). Editing /etc/sysconfig/Jenkins ensures consistent credential availability across Jenkins jobs while supporting secure integration with AWS services during the Code stage.

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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Running an SSH service inside Docker containers is considered a security anti-pattern because it increases the attack surface and complicates key and access management. Containers are designed to run a single primary process and be managed externally rather than accessed via SSH. The recommended solution is torun SSH on the host systemand use docker exec to interact with containers when administrative access is required. This approach eliminates the need to manage SSH keys inside containers, reduces exposure to brute-force attacks, and simplifies access control. The other options incorrectly suggest running SSH in inappropriate locations such as the registry, client, or build process, which do not address the core security concern. During the Operate and Monitor stage, minimizing unnecessary services within containers is critical to enforcing least privilege and maintaining a secure runtime environment.

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To identify the currently running Java binary used by Jenkins, administrators typically inspect the running processes on the Linux system. The command ps -aux | grep Jenkins lists all active processes and filters those related to Jenkins. This output includes details such as the user, process ID, and the full command line used to start Jenkins, which often contains the path to the Java executable. The other options reference invalid or nonexistent commands (pc, as, ac). Locating the Java binary is necessary when installing certificates into the correct Java keystore, which is required for secure communication between Jenkins and tools like Acunetix. Performing this setup during the Build and Test stage ensures that DAST scans can run securely and reliably as part of automated pipelines.

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In a mature DevSecOps pipeline, security controls are enforced asgates, not merely as informational checks. When an SCA tool detects acritical vulnerabilityin a dependency—whether direct or transitive—the correct response at the Build and Test stage is tofail the build. This prevents vulnerable artifacts from moving forward into later stages such as deployment or production, where remediation would be more expensive and risky. Allowing the build to continue, even with notifications, contradicts the shift-left security principle. Ignoring transitive dependencies is also dangerous, as many real-world vulnerabilities originate from indirect libraries. Failing the build forces developers to remediate the issue immediately by upgrading, replacing, or mitigating the vulnerable dependency. This approach reduces attack surface, enforces accountability, and ensures that only secure artifacts are released. Therefore, stopping the pipeline upon detection of critical vulnerabilities reflects a strong DevSecOps maturity model and effective security governance.

Docker allows inspection of container runtime configuration using the docker inspect command. To list security-related options such as no_new_privileges for all containers, the correct approach is to first retrieve all container IDs using docker ps --quiet --all and then pass them to docker inspect with a formatted output. The command docker ps --quiet --all | xargs docker inspect --format ': SecurityOpt=' correctly extracts the security options configured for each container. Options that use incorrect flags such as -quiet instead of --quiet, omit required parameters, or misformat the output string are invalid. Inspecting security options during the Operate and Monitor stage helps ensure that privilege escalation protections are enforced consistently, supporting container hardening and compliance with security benchmarks.

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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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Jenkins automatically assigns a unique identifier to each build using the environment variableBUILD_ID. When integrating OWASP ZAP with Jenkins, appending ${BUILD_ID} to output filenames or reports ensures that every scan result corresponds to a specific build execution. This avoids overwriting previous reports and allows traceability between build artifacts and security findings. Variables such as ${ZAPROXY_HOME} refer to installation paths, not build uniqueness, while ${Profile_ID} and ${zap_scan} are not standard Jenkins variables for uniquely identifying builds. Using ${BUILD_ID} supports better auditing, historical analysis, and correlation between detected vulnerabilities and the exact build in which they were found, which is critical during the Build and Test stage of a DevSecOps pipeline.

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w3af (Web Application Attack and Audit Framework) is aDynamic Application Security Testing (DAST)tool. It analyzes running web applications by sending crafted requests and observing responses to identify vulnerabilities such as SQL injection, cross-site scripting, and authentication flaws. Unlike SAST tools, w3af does not require access to source code and instead operates externally, simulating real-world attack behavior. SCA focuses on third-party dependencies, and IAST requires runtime instrumentation within the application. Since Debra detected vulnerabilities by actively interacting with the application, w3af clearly represents DAST. DAST tools are especially valuable during the Build and Test stage, as they validate application behavior from an attacker’s perspective before deployment.

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The command shown in the pre-build phase explicitly targets a script namedwss_agent.sh. The curl -LJO flags mean: -L follows redirects, -J honors the server-provided filename in the Content-Disposition header (when present), and -O writes output to a local file using the remote name. Since the requested path ends with wss_agent.sh, the downloaded file on the AWS CodeBuild server will be wss_agent.sh. This script is the WhiteSource (now commonly referred to as Mend in many environments) unified agent shell wrapper used to run SCA scans as part of a CI pipeline. Integrating SCA during the Build and Test stage helps detect vulnerable open-source dependencies and licensing/compliance issues early, when fixes are cheapest. The other filenames (ssw_agent.sh, cbs_agent.sh, aws_agent.sh) are distractors; they are not referenced by the provided command and would not be downloaded by that step.

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Syft is used by Anchore to generate Software Bill of Materials (SBOMs) from container images and directories. By default, Syft may only analyze the squashed image view. Using the --scope all-layers flag instructs Syft to include software components fromall image layers, ensuring comprehensive visibility into dependencies introduced at every stage of image creation. The other options use invalid syntax or unsupported flags. Including all layers during SBOM generation improves vulnerability detection accuracy and supports compliance requirements, making it a critical practice during the Build and Test stage.

ThreatPlaybook uses the playbook apply feature command to push user-story–driven threat models to the server. The -f flag specifies the path to the YAML file containing the defined user stories, abuser stories, and threat scenarios, while the -p flag specifies the target project. Option C correctly combines these parameters. The -y flag is invalid in this context, and options that misuse -t instead of -p do not correctly identify the project destination. Executing this command during the Plan stage enables teams to integrate threat modeling early, ensuring security risks are identified and addressed before development and deployment proceed.

HashiCorp Vault enables authentication mechanisms using the vault auth enable command followed by the name of the authentication method. To enable AppRole authentication, the correct command is vault auth enable approle. AppRole is specifically designed for machine-to-machine authentication, making it ideal for CI/CD tools like Jenkins. It allows applications to authenticate securely using role IDs and secret IDs instead of static credentials. The other options do not follow Vault CLI syntax and would result in command errors. Enabling AppRole during the Build and Test stage ensures that secrets are accessed securely and dynamically, supporting least-privilege access control and reducing the risk of credential leakage across the DevSecOps pipeline.

ThreatPlaybook CLI is configured using the ThreatPlaybook configure command, which initializes the CLI with the required connection and user details. The -e option is used to specify the user’s email address, the -h option defines the host information such as IP address or hostname, and the -p option specifies the port number. This configuration enables the CLI to securely communicate with the ThreatPlaybook service for orchestrating threat modeling and application security testing workflows. Options that use playbook configure are incorrect because the executable name is explicitly ThreatPlaybook. Options using -u instead of -h do not correctly specify host information. Configuring ThreatPlaybook during the Plan stage helps teams adopt an attacker’s mindset early, embedding security into the organization’s culture and ensuring threats are identified and addressed before development and deployment activities begin.

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BeSECURE is a vulnerability management and assessment solution designed for continuous scanning of both network and application vulnerabilities. It emphasizes flexibility, accuracy, low maintenance overhead, and frequent updates to vulnerability detection mechanisms. These characteristics align directly with the scenario described, where the organization requires continuous scanning, daily updates, and specialized testing methodologies to detect a wide range of vulnerabilities. SonarQube focuses on static code quality and security analysis during development, Black Duck is primarily used for open-source software composition analysis, and Shadow Daemon is a web application firewall rather than a comprehensive vulnerability management solution. Using BeSECURE during the Operate and Monitor stage allows organizations to maintain ongoing visibility into their security posture, detect new vulnerabilities as they emerge, and reduce the likelihood of repeat attacks by addressing weaknesses proactively.

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Dynamic Application Security Testing (DAST) and fuzz testing require a running application in order to actively probe for vulnerabilities such as injection flaws, authentication bypasses, and improper input handling. These techniques are therefore performed after the application has been built and deployed to a testing environment, categorizing them astest-time checks. Commit-time and build-time checks rely primarily on static analysis and dependency scanning and do not exercise application behavior at runtime. Deploy-time checks focus on configuration validation rather than aggressive attack simulation. Test-time checks are specifically designed to uncover critical and high-severity vulnerabilities by mimicking real-world attack scenarios. Performing DAST and fuzz testing during this stage allows teams to detect exploitable flaws before production release, significantly strengthening application security.

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In AWS-based DevSecOps pipelines, static analysis tools such as PHPStan commonly send their results to AWS services through event-driven processing. When PHPStan detects security issues, the results are typically parsed and processed by anAWS Lambda function, which can transform findings and forward them to AWS Security Hub. CloudFormation is used for infrastructure provisioning, AWS Config evaluates configuration compliance, and Elastic Beanstalk is an application deployment service—none of these are suited for parsing and relaying scan results. Lambda functions provide a scalable and serverless way to handle scan outputs automatically. This integration ensures that security findings are centralized, visible, and actionable, aligning with secure automation practices during the Code stage.

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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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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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Prisma Cloud leveragesadvanced unsupervised machine learningto establish baselines of normal behavior within a customer’s cloud environment. By analyzing patterns in network traffic, resource interactions, and workload behavior without relying on labeled training data, it can detect anomalies and potential zero-day attacks with minimal false positives. Supervised approaches require predefined labels and known attack patterns, which limits effectiveness against new or unknown threats. Unsupervised data mining alone lacks the adaptive intelligence provided by machine learning models. Using unsupervised machine learning during the Build and Test stage enables continuous, intelligent security analysis across dynamic cloud-native workloads, supporting proactive threat detection in DevSecOps pipelines.

HashiCorp Packer is an image automation tool that uses the packer build command to create machine images from configuration files written in HCL or JSON. When Robin defines her Docker image configuration in the file docker-ubuntu.pkr.hcl, the correct way to initiate the build process is by running packer build docker-ubuntu.pkr.hcl. This command reads the configuration file, initializes required plugins, executes defined builders and provisioners, and produces the final Docker image. The other options are syntactically incorrect because Packer does not support abbreviated flags such as -b or alternative verbs like -build. Building container images during the Build and Test stage ensures that images are reproducible, standardized, and compliant with organizational security requirements before deployment. Using Packer also supports immutability and reduces configuration drift, which are key principles in secure DevSecOps pipelines.

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When working inside an interactive Docker container session, the container continues running as long as its primary foreground process is active. Executing the exit command terminates the shell session, which in turn stops the container if no other foreground processes are running. The kill command requires a process identifier and is not used in this context, while clear simply clears the terminal screen and does not affect container execution. The stop command is not a valid shell command inside a container. Properly stopping containers during the Operate and Monitor stage helps free system resources, prevent unintended service exposure, and maintain a clean runtime environment. This practice aligns with container lifecycle management best practices and reduces operational risk.

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GitLab CI/CD pipelines are defined using a configuration file namedgitlab-ci.yml, which must be placed in the root directory of the repository. This file controls pipeline stages, jobs, and template inclusions. To integrate Checkmarx SCA using a predefined template, the template reference must be included in the root-level gitlab-ci.yml file so GitLab can load and execute the defined jobs automatically. The other filenames listed in the options are not recognized by GitLab as valid pipeline configuration files. Integrating SCA at the Code stage allows early detection of vulnerable open-source dependencies, reducing remediation cost and preventing insecure components from progressing further in the DevSecOps pipeline.

Gauntlt is a security testing framework written in Ruby and distributed as a Ruby gem. The correct way to install a Ruby gem is using the gem install command followed by the lowercase gem name. RubyGems are case-sensitive and standardized to lowercase naming conventions, which makes gem install gauntlt the correct command. The gems command does not exist in Ruby’s package management ecosystem, and using uppercase names such as Gauntlt can lead to installation failures. Installing Gauntlt allows DevSecOps teams to write human-readable security tests and integrate them into CI/CD pipelines, enabling automated and collaborative security validation during the Build and Test stage.

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