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CyberArk CAU305 Exam Reference

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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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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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CAU305 Exam topics

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• Measuring, monitoring, and troubleshooting machine learning models

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

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

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

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

• Designing for security and compliance

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

• Building and running test suites

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

• Resizing and autoscaling resources

• What changes can be performed on statuses and transitions?

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

• Target Hosts Using Privileged Session Manager

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

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

• Ensuring flexibility and portability

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

• Distributed vs. single machine

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

• Use of edge compute

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

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

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

• Ensuring scalability and efficiency

• Choosing the appropriate training and serving infrastructure

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

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

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

• Continuous evaluation

• Securely Managing the System Architecture

• Data staging, cataloging, and discovery

• Leveraging pre-built ML models as a service

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

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

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

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

• Deploying an ML pipeline

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

• What is the importance of the Browse Projects permission?

• 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?

• Ensuring reliability and fidelity

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

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

• Verification and monitoring

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

• Monitoring Privileged Account Access

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

• Conversational experiences (e.g., Dialogflow)

• Defining the CyberArk Privileged Account Security Solution Architecture

• Ingesting appropriate data

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

• Operationalizing machine learning models

• Choosing between ACID, idempotent, eventually consistent requirements

• Pipeline monitoring (e.g., Stackdriver)

• Data security (encryption, key management)

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

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

• What are the uses of Project Category in Jira?

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

• Impact of dependencies of machine learning models

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

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

• Mapping to current and future business requirements