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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How to study the CAU305 Exam

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CAU305 Exam topics

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

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

• Operationalizing machine learning models

• 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?

• Impact of dependencies of machine learning models

• Ensuring reliability and fidelity

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

• What is the importance of the Browse Projects permission?

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

• Target Hosts Using Privileged Session Manager

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

• Building and running test suites

• Pipeline monitoring (e.g., Stackdriver)

• What are the uses of Project Category in Jira?

• Verification and monitoring

• Securely Managing the System Architecture

• Mapping to current and future business requirements

• Designing for security and compliance

• Ensuring scalability and efficiency

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

• Monitoring Privileged Account Access

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

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

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

• 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

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

• Data security (encryption, key management)

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

• Resizing and autoscaling resources

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

• 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?

• Defining the CyberArk Privileged Account Security Solution Architecture

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

• Distributed vs. single machine

• Choosing between ACID, idempotent, eventually consistent requirements

• 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

• Ingesting appropriate data

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

• Continuous evaluation

• Measuring, monitoring, and troubleshooting machine learning models

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

• Leveraging pre-built ML models as a service

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

• Choosing the appropriate training and serving infrastructure

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

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

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

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

• Conversational experiences (e.g., Dialogflow)

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

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

• Data staging, cataloging, and discovery

• What changes can be performed on statuses and transitions?

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

• Ensuring flexibility and portability

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

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

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

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