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The CyberArk CDE certification is valid for 18 months. Certified Delivery Engineers will get automatic notifications 6 months and 3 months before their certification is expired. To get re-certified, CyberArk Certified Delivery Engineers should pass the CAU305: CyberArk CDE Recertification exam.

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Recertifications are an important part of maintaining credentials and status of the candidate. Practice exams and knowledge exam dumps are built to make students comfortable with the real exam environment. PassCollection expert team recommends you to prepare notes on these topics as you go along preparing with it and don't forget to practice CAU-305 practice exams and revise CAU-305 exam dumps.

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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.

• Use of edge compute

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

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

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

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

• Conversational experiences (e.g., Dialogflow)

• Ingesting appropriate data

• Building and running test suites

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

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

• Measuring, monitoring, and troubleshooting machine learning models

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

• Resizing and autoscaling resources

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

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

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

• Securely Managing the System Architecture

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

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

• Leveraging pre-built ML models as a service

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

• Verification and monitoring

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

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

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

• Ensuring reliability and fidelity

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

• Mapping to current and future business requirements

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

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

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

• Choosing the appropriate training and serving infrastructure

• What are the uses of Project Category in Jira?

• Continuous evaluation

• Choosing between ACID, idempotent, eventually consistent requirements

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

• What changes can be performed on statuses and transitions?

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

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

• Impact of dependencies of machine learning models

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

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

• Ensuring flexibility and portability

• Data staging, cataloging, and discovery

• Target Hosts Using Privileged Session Manager

• Pipeline monitoring (e.g., Stackdriver)

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

• Operationalizing machine learning models

• Ensuring scalability and efficiency

• Data security (encryption, key management)

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

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

• Deploying an ML pipeline

• Designing for security and compliance

• Monitoring Privileged Account Access

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

• 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?

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

• What is the importance of the Browse Projects permission?

• Defining the CyberArk Privileged Account Security Solution Architecture

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

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

• Distributed vs. single machine

CyberArk CAU305日本語 Exam Syllabus Topics: