CyberArk CAU305 Exam Overview:

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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Certification Path

If your CDE certificate is expiring then CyberArk CDE recertification is the way to continue having the credentials.

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.

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

• Defining the CyberArk Privileged Account Security Solution Architecture

• Operationalizing machine learning models

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

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

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

• 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?

• Choosing the appropriate training and serving infrastructure

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

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

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

• What is the importance of the Browse Projects permission?

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

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

• Building and running test suites

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

• Designing for security and compliance

• 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 changes can be performed on statuses and transitions?

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

• Conversational experiences (e.g., Dialogflow)

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

• Verification and monitoring

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

• Ensuring scalability and efficiency

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

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

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

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

• Target Hosts Using Privileged Session Manager

• What are the uses of Project Category in Jira?

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

• Pipeline monitoring (e.g., Stackdriver)

• Distributed vs. single machine

• Ensuring flexibility and portability

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

• Monitoring Privileged Account Access

• Use of edge compute

• Leveraging pre-built ML models as a service

• Choosing between ACID, idempotent, eventually consistent requirements

• Data staging, cataloging, and discovery

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

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

• Measuring, monitoring, and troubleshooting machine learning models

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

• Data security (encryption, key management)

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

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

• Ensuring reliability and fidelity

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

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

• Deploying an ML pipeline

• Mapping to current and future business requirements

• Resizing and autoscaling resources

• Continuous evaluation

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

• Ingesting appropriate data

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

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

• Impact of dependencies of machine learning models

• Securely Managing the System Architecture

What is the duration of the CAU305 Exam

• Format: Multiple choices, multiple answers
• Passing Score: 70%
• Length of Examination: 90 minutes
• Number of Questions: 40

CyberArk CAU305 Exam Syllabus Topics: