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How to Prepare for CyberArk CDE Recertification CAU305 Exam
Preparation Guide for CyberArk CDE Recertification CAU305 Exam
Introduction
CyberArk CDE Recertification CAU305 Exam is related to CyberArk CDE recertification.
This program is specially designed for aspirants who are really interested in earning CyberArk stars and can start working as PAM technology experts. This shows knowledge of end to end implementation of CyberArk Privilege Access Security Solution.
Vault Administrators, IT Personnel, CyberArk PAS Consultants usually hold or pursue this certification and you can expect the same job role after completion of this certification.
The CyberArk Certification Program offers a variety of options to those who want recognition for their technical expertise. The CyberArk Certification Program, hosted and proctored by Pearson VUE*, offers multi-level industry certifications covering privileged account security. CyberArk offers certifications designed for various roles and experience, each introducing progressively more advanced and challenging material and exams. You can work up to the level that best aligns with your responsibilities and career ambitions.
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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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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 all the various factors that influence whether a field is visible on a screen?
Distributed vs. single machine
Ensuring scalability and efficiency
Hardware accelerators (e.g., GPU, TPU)
Ensuring reliability and fidelity
Identity and access management (e.g.,Cloud IAM)
Pipeline monitoring (e.g., Stackdriver)
Assessing, troubleshooting, and improving data representations and data processing infrastructure
What is an Issue Collector and what permissions are needed to configure it?
Ensuring privacy (e.g., Data Loss Prevention API)
Impact of dependencies of machine learning models
Choosing the appropriate training and serving infrastructure
Resizing and autoscaling resources
What changes can be performed on statuses and transitions?
What are the uses of Project Category in Jira?
Measuring, monitoring, and troubleshooting machine learning models
Target Hosts Using Privileged Session Manager
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
Which export options are available from the Issue Navigation? The following will be discussed here:
Use of edge compute
Performing data preparation and quality control (e.g., Cloud Dataprep)
Which project details can be added and changed by Project Administrators?
Verification and monitoring
Who can change the Simplified Workflow? What are the limitations on changes to the Simplified Workflow?
How are valid JQL queries written? What operators and arguments are valid for various JQL functions (e.g. membersOf(), StartOfDay(), WAS, CHANGED, etc.)
What workflow changes can be done through the board by board administrators?
Ensuring flexibility and portability
Securely Managing the System Architecture
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
Common sources of error (e.g., assumptions about data)
Defining the CyberArk Privileged Account Security Solution Architecture
Operationalizing machine learning models
What is the importance of the Browse Projects permission?
Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
Mapping to current and future business requirements
What types of workflow changes can Project Administrators make if Extended Project Administration is activated?
Monitoring Privileged Account Access
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
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?
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Data staging, cataloging, and discovery
Leveraging pre-built ML models as a service
ML APIs (e.g., Vision API, Speech API)
Continuous evaluation
Data security (encryption, key management)
Designing for security and compliance
What are the use cases where it is best to use Project Roles rather than Groups?
What is the relationship between screens, screen schemes and issue type screen schemes?
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
Which business requirements necessitate the use of separate issue types in a project (e.g. different screens, workflows, etc.)?
When is it best to use components, and when is it best to use a custom field instead?
How do combinations of permissions work together to enable specific actions?
Ingesting appropriate data
Designing for data and application portability (e.g., multi-cloud, data residency requirements)
Building and running test suites
What things may need to be updated as a result of workflow changes of any kind?
Where are all the places you need to look to fully identify what permissions a particular user has?
Choosing between ACID, idempotent, eventually consistent requirements
What are the features, uses, limitations/restrictions of workflow changes, especially the Simplified Workflow?
Conversational experiences (e.g., Dialogflow)
