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

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

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

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

• Data security (encryption, key management)

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

• Choosing between ACID, idempotent, eventually consistent requirements

• Ensuring reliability and fidelity

• Conversational experiences (e.g., Dialogflow)

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

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

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

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

• Defining the CyberArk Privileged Account Security Solution Architecture

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

• What is the importance of the Browse Projects permission?

• Pipeline monitoring (e.g., Stackdriver)

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

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

• Distributed vs. single machine

• Designing for security and compliance

• Ingesting appropriate data

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

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

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

• Mapping to current and future business requirements

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

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

• 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

• Measuring, monitoring, and troubleshooting machine learning models

• Leveraging pre-built ML models as a service

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

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

• Data staging, cataloging, and discovery

• Verification and monitoring

• Monitoring Privileged Account Access

• 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)

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

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

• Operationalizing machine learning models

• Continuous evaluation

• 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 uses of Project Category in Jira?

• Ensuring scalability and efficiency

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

• Choosing the appropriate training and serving infrastructure

• What changes can be performed on statuses and transitions?

• Building and running test suites

• Deploying an ML pipeline

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

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

• Impact of dependencies of machine learning models

• Ensuring flexibility and portability

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

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

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

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

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

• Use of edge compute

• Resizing and autoscaling resources

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Difficulty in writing CAU305 Exam

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