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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.

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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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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.

• Data staging, cataloging, and discovery

• Designing for security and compliance

• Continuous evaluation

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

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

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

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

• Ensuring flexibility and portability

• Measuring, monitoring, and troubleshooting machine learning models

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

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

• Verification and monitoring

• Choosing between ACID, idempotent, eventually consistent requirements

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

• What are the uses of Project Category in Jira?

• Defining the CyberArk Privileged Account Security Solution Architecture

• Pipeline monitoring (e.g., Stackdriver)

• Target Hosts Using Privileged Session Manager

• Ingesting appropriate data

• Resizing and autoscaling resources

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

• Data security (encryption, key management)

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

• Operationalizing machine learning models

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

• Securely Managing the System Architecture

• Ensuring scalability and efficiency

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

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

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

• What changes can be performed on statuses and transitions?

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

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

• Building and running test suites

• Monitoring Privileged Account Access

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

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

• Ensuring reliability and fidelity

• 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 the configuration options for all the out-of-box reports and gadgets (except Agile reports and gadgets)? What is their behavior and expected results?

• Use of edge compute

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

• Impact of dependencies of machine learning models

• Deploying an ML pipeline

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

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

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

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

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

• Mapping to current and future business requirements

• Distributed vs. single machine

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

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

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

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

• Leveraging pre-built ML models as a service

• What is the importance of the Browse Projects permission?

• Choosing the appropriate training and serving infrastructure

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

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

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

• Conversational experiences (e.g., Dialogflow)

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

CyberArk CAU305 Exam Syllabus Topics: