CyberArk CDE Recertification : CAU305

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Certification Path

If your CDE certificate is expiring then CyberArk CDE recertification is the way to continue having the credentials.

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The benefit in Obtaining the CAU305 Exam Certification

The recertification exam provides the practical knowledge and technical skills to securely design, install, and configure the CyberArk Privileged Access Security Solution. The candidates will gain valuable hands-on experience installing CyberArk components, configuring Authentication Types, and System Integrations (SIEM, SMTP, SNMP), using our step-by-step exercise guide, official product documentation and a dedicated lab environment.

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CAU305 Exam topics

Candidates must know the Defender + Sentry exam topics before they start of preparation because it will really help them in hitting the core. Our CAU-305 practice test and CAU-305 practice exams will include the following topics. These are covered in our CAU-305 exam dumps.

• Common sources of error (e.g., assumptions about data)

• Which export options are available from the Issue Navigation? The following will be discussed here:

• What are the use cases where it is best to use Project Roles rather than Groups?

• Building and running test suites

• Operationalizing machine learning models

• Target Hosts Using Privileged Session Manager

• Monitoring Privileged Account Access

• Which configurations (schemes, versions, components, etc) are shared versus unique when new projects are (1) created from template or (2) with a shared configuration?

• Designing for data and application portability (e.g., multi-cloud, data residency requirements)

• Defining the CyberArk Privileged Account Security Solution Architecture

• How are valid JQL queries written? What operators and arguments are valid for various JQL functions (e.g. membersOf(), StartOfDay(), WAS, CHANGED, etc.)

• What are all the various factors that influence whether a field is visible on a screen?

• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)

• Measuring, monitoring, and troubleshooting machine learning models

• What are the features, uses, limitations/restrictions of workflow changes, especially the Simplified Workflow?

• When is it best to use components, and when is it best to use a custom field instead?

• Identity and access management (e.g.,Cloud IAM)

• Choosing between ACID, idempotent, eventually consistent requirements

• Ensuring reliability and fidelity

• What is the importance of the Browse Projects permission?

• Who can change a Project Key, and what are the effects on issues in the project?

• Where are all the places you need to look to fully identify what permissions a particular user has?

• Which business requirements necessitate the use of separate issue types in a project (e.g. different screens, workflows, etc.)?

• Who can change the Simplified Workflow? What are the limitations on changes to the Simplified Workflow?

• Designing for security and compliance

• What are the uses of Project Category in Jira?

• Leveraging pre-built ML models as a service

• How do combinations of permissions work together to enable specific actions?

• Hardware accelerators (e.g., GPU, TPU)

• Ensuring scalability and efficiency

• Distributed vs. single machine

• Ensuring flexibility and portability

• Use of edge compute

• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)

• Pipeline monitoring (e.g., Stackdriver)

• Verification and monitoring

• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)

• What changes can be performed on statuses and transitions?

• Mapping to current and future business requirements

• Resizing and autoscaling resources

• What are the configuration options for all the out-of-box reports and gadgets (except Agile reports and gadgets)? What is their behavior and expected results?

• Choosing the appropriate training and serving infrastructure

• Continuous evaluation

• What is an Issue Collector and what permissions are needed to configure it?

• Conversational experiences (e.g., Dialogflow)

• What types of workflow changes can Project Administrators make if Extended Project Administration is activated?

• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)

• What is the relationship between screens, screen schemes and issue type screen schemes?

• Assessing, troubleshooting, and improving data representations and data processing infrastructure

• Impact of dependencies of machine learning models

• Securely Managing the System Architecture

• Ensuring privacy (e.g., Data Loss Prevention API)

• Which project details can be added and changed by Project Administrators?

• Data security (encryption, key management)

• What workflow changes can be done through the board by board administrators?

• ML APIs (e.g., Vision API, Speech API)

• Performing data preparation and quality control (e.g., Cloud Dataprep)

• Ingesting appropriate data

• What things may need to be updated as a result of workflow changes of any kind?

• What features are available for Jira Software projects, and to users who have Jira Software application access (as compared to just Jira Core)?

• Deploying an ML pipeline

• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))

• Data staging, cataloging, and discovery

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CyberArk CAU305 Exam Syllabus Topics: