What is the duration of the CAU305 Exam

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

Certification Path

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

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.

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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.

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

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

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

• Securely Managing the System Architecture

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

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

• What is the importance of the Browse Projects permission?

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

• Designing for security and compliance

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

• Leveraging pre-built ML models as a service

• Measuring, monitoring, and troubleshooting machine learning models

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

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

• Defining the CyberArk Privileged Account Security Solution Architecture

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

• Building and running test suites

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

• Deploying an ML pipeline

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

• Impact of dependencies of machine learning models

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

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

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

• Verification and monitoring

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

• Ensuring scalability and efficiency

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

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

• Data staging, cataloging, and discovery

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

• Operationalizing machine learning models

• Monitoring Privileged Account Access

• Distributed vs. single machine

• Ingesting appropriate data

• Ensuring flexibility and portability

• Data security (encryption, key management)

• What are the uses of Project Category in Jira?

• Conversational experiences (e.g., Dialogflow)

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

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

• Use of edge compute

• Ensuring reliability and fidelity

• Continuous evaluation

• Mapping to current and future business requirements

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

• Choosing the appropriate training and serving infrastructure

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

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

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

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

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

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

• Pipeline monitoring (e.g., Stackdriver)

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

• Resizing and autoscaling resources

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

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

• 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?

• Target Hosts Using Privileged Session Manager

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

• What changes can be performed on statuses and transitions?

• Choosing between ACID, idempotent, eventually consistent requirements