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.

Certification Path

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

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.

• Leveraging pre-built ML models as a service

• Distributed vs. single machine

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

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

• Choosing the appropriate training and serving infrastructure

• Data staging, cataloging, and discovery

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

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

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

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

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

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

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

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

• Pipeline monitoring (e.g., Stackdriver)

• Choosing between ACID, idempotent, eventually consistent requirements

• Monitoring Privileged Account Access

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

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

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

• What changes can be performed on statuses and transitions?

• Impact of dependencies of machine learning models

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

• Ingesting appropriate data

• Resizing and autoscaling resources

• What is the importance of the Browse Projects permission?

• Continuous evaluation

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

• Measuring, monitoring, and troubleshooting machine learning models

• Defining the CyberArk Privileged Account Security Solution Architecture

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

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

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

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

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

• Ensuring reliability and fidelity

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

• Designing for security and compliance

• Ensuring flexibility and portability

• Ensuring scalability and efficiency

• Operationalizing machine learning models

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

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

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

• Verification and monitoring

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

• Use of edge compute

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

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

• Deploying an ML pipeline

• Target Hosts Using Privileged Session Manager

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

• 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?

• Data security (encryption, key management)

• Securely Managing the System Architecture

• Building and running test suites

• Conversational experiences (e.g., Dialogflow)

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

• Mapping to current and future business requirements

• What are the uses of Project Category in Jira?

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

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