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.

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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 reliability and fidelity

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

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

• Leveraging pre-built ML models as a service

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

• What are the uses of Project Category in Jira?

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

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

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

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

• Monitoring Privileged Account Access

• What changes can be performed on statuses and transitions?

• Choosing between ACID, idempotent, eventually consistent requirements

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

• 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?

• Mapping to current and future business requirements

• Deploying an ML pipeline

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

• Resizing and autoscaling resources

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

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

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

• Pipeline monitoring (e.g., Stackdriver)

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

• Verification and monitoring

• Ingesting appropriate data

• Distributed vs. single machine

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

• Building and running test suites

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

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

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

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

• Operationalizing machine learning models

• Choosing the appropriate training and serving infrastructure

• Target Hosts Using Privileged Session Manager

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

• Securely Managing the System Architecture

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

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

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

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

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

• Ensuring scalability and efficiency

• Conversational experiences (e.g., Dialogflow)

• Measuring, monitoring, and troubleshooting machine learning models

• Ensuring flexibility and portability

• Defining the CyberArk Privileged Account Security Solution Architecture

• Data security (encryption, key management)

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

• Continuous evaluation

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

• What is the importance of the Browse Projects permission?

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

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

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

• Impact of dependencies of machine learning models

• Data staging, cataloging, and discovery

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

• Use of edge compute

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

• Designing for security and compliance

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

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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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