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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.

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

• Data staging, cataloging, and discovery

• Verification and monitoring

• What are the uses of Project Category in Jira?

• Ensuring reliability and fidelity

• Monitoring Privileged Account Access

• Defining the CyberArk Privileged Account Security Solution Architecture

• Leveraging pre-built ML models as a service

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

• Ensuring scalability and efficiency

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

• Continuous evaluation

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

• Ingesting appropriate data

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

• Building and running test suites

• Conversational experiences (e.g., Dialogflow)

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

• Choosing between ACID, idempotent, eventually consistent requirements

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

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

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

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

• 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?

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

• 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)?

• Distributed vs. single machine

• Ensuring flexibility and portability

• Securely Managing the System Architecture

• 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?

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

• 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?

• What changes can be performed on statuses and transitions?

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

• Measuring, monitoring, and troubleshooting machine learning models

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

• Choosing the appropriate training and serving infrastructure

• Target Hosts Using Privileged Session Manager

• Resizing and autoscaling resources

• Use of edge compute

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

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

• Mapping to current and future business requirements

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

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

• Deploying an ML pipeline

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

• Designing for security and compliance

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

• Impact of dependencies of machine learning models

• Operationalizing machine learning models

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

• 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?

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

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

• Data security (encryption, key management)

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

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

• Pipeline monitoring (e.g., Stackdriver)

How much CAU305 Exam Cost

The price of the CAU305 exam is $200 USD.

Certification Path

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

Difficulty in writing CAU305 Exam

Candidates already have experience with Sentry and Defender examination so it is not difficult to pass the recertifications. CAU-305 exam dumps and CAU-305 practice exams to revise the already discussed syllabus.