CyberArk CDE Recertification : CAU305

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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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What is the duration of the CAU305 Exam

• Format: Multiple choices, multiple answers
• Length of Examination: 90 minutes
• Passing Score: 70%
• Number of Questions: 40

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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.

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

• Defining the CyberArk Privileged Account Security Solution Architecture

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

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

• Ensuring scalability and efficiency

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

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

• Target Hosts Using Privileged Session Manager

• What are the uses of Project Category in Jira?

• Verification and monitoring

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

• Securely Managing the System Architecture

• What is the importance of the Browse Projects permission?

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

• What changes can be performed on statuses and transitions?

• Impact of dependencies of machine learning models

• Pipeline monitoring (e.g., Stackdriver)

• Designing for security and compliance

• Conversational experiences (e.g., Dialogflow)

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

• Data staging, cataloging, and discovery

• Choosing the appropriate training and serving infrastructure

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

• Ingesting appropriate data

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

• Ensuring reliability and fidelity

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

• Choosing between ACID, idempotent, eventually consistent requirements

• Mapping to current and future business requirements

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

• Resizing and autoscaling resources

• Leveraging pre-built ML models as a service

• Data security (encryption, key management)

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

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

• Measuring, monitoring, and troubleshooting machine learning models

• Building and running test suites

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

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

• Deploying an ML pipeline

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

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

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

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

• Distributed vs. single machine

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

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

• Operationalizing machine learning models

• Monitoring Privileged Account Access

• Ensuring flexibility and portability

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

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

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

• Use of edge compute

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

• Continuous evaluation

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

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

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

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

• 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?

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

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

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CyberArk CAU305 Exam Reference

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CyberArk CAU305 Exam Syllabus Topics: