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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.

• Ensuring scalability and efficiency

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

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

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

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

• Ensuring flexibility and portability

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

• Continuous evaluation

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

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

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

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

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

• Impact of dependencies of machine learning models

• Choosing the appropriate training and serving infrastructure

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

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

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

• Distributed vs. single machine

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

• Ensuring reliability and fidelity

• Building and running test suites

• Data security (encryption, key management)

• Conversational experiences (e.g., Dialogflow)

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

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

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

• Use of edge compute

• Resizing and autoscaling resources

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

• Choosing between ACID, idempotent, eventually consistent requirements

• 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?

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

• Mapping to current and future business requirements

• What changes can be performed on statuses and transitions?

• Ingesting appropriate data

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

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

• Operationalizing machine learning models

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

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

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

• Securely Managing the System Architecture

• Leveraging pre-built ML models as a service

• What is the importance of the Browse Projects permission?

• Deploying an ML pipeline

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

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

• What are the uses of Project Category in Jira?

• Defining the CyberArk Privileged Account Security Solution Architecture

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

• Measuring, monitoring, and troubleshooting machine learning models

• Data staging, cataloging, and discovery

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

• Pipeline monitoring (e.g., Stackdriver)

• Verification and monitoring

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

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

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

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

• Monitoring Privileged Account Access

• Designing for security and compliance

• Target Hosts Using Privileged Session Manager

What is the duration of the CAU305 Exam

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

Who should take the CAU305 exam

The CyberArk CDE certification is valid for 18 months. Certified Delivery Engineers will get automatic notifications 6 months and 3 months before their certification is expired. To get re-certified, CyberArk Certified Delivery Engineers should pass the CAU305: CyberArk CDE Recertification exam.