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• Deploying an ML pipeline

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

• Mapping to current and future business requirements

• Resizing and autoscaling resources

• What are the uses of Project Category in Jira?

• Use of edge compute

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

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

• What changes can be performed on statuses and transitions?

• Ensuring flexibility and portability

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

• Designing for security and compliance

• Building and running test suites

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

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

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

• 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?

• Pipeline monitoring (e.g., Stackdriver)

• Monitoring Privileged Account Access

• Securely Managing the System Architecture

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

• Ensuring reliability and fidelity

• 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?

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

• Continuous evaluation

• Target Hosts Using Privileged Session Manager

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

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

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

• Choosing the appropriate training and serving infrastructure

• Leveraging pre-built ML models as a service

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

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

• Choosing between ACID, idempotent, eventually consistent requirements

• Measuring, monitoring, and troubleshooting machine learning models

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

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

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

• Verification and monitoring

• Operationalizing machine learning models

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

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

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

• Ingesting appropriate data

• Ensuring scalability and efficiency

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

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

• What is the importance of the Browse Projects permission?

• Impact of dependencies of machine learning models

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

• Conversational experiences (e.g., Dialogflow)

• Distributed vs. single machine

• Data staging, cataloging, and discovery

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

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

• Defining the CyberArk Privileged Account Security Solution Architecture

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

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

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

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

• Data security (encryption, key management)

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CyberArk CAU305 Exam Reference

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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It is necessary to be nicely ready for passing the CyberArk CDE Recertification as one particular has to answer questions reasonably and within the restricted time interval offered by the examiner. So to make sure the passing on the exam candidate need to fully recognize the NCP 5 exam questions pattern and sort of questions that appear on CyberArk CDE Recertification to answer explicitly.

CyberArk CAU305 Exam Syllabus Topics: