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.

• Pipeline monitoring (e.g., Stackdriver)

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

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

• Defining the CyberArk Privileged Account Security Solution Architecture

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

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

• Ensuring flexibility and portability

• Data staging, cataloging, and discovery

• Ensuring reliability and fidelity

• What are the uses of Project Category in Jira?

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

• Mapping to current and future business requirements

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

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

• Conversational experiences (e.g., Dialogflow)

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

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

• Operationalizing machine learning models

• 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.)

• 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)

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

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

• Use of edge compute

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

• Continuous evaluation

• 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 is the importance of the Browse Projects permission?

• Securely Managing the System Architecture

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

• Resizing and autoscaling resources

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

• 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)

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

• Ingesting appropriate data

• Designing for security and compliance

• Distributed vs. single machine

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

• Deploying an ML pipeline

• Verification and monitoring

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

• Leveraging pre-built ML models as a service

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

• Ensuring scalability and efficiency

• Measuring, monitoring, and troubleshooting machine learning models

• Building and running test suites

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

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

• Choosing between ACID, idempotent, eventually consistent requirements

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

• Impact of dependencies of machine learning models

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

• Choosing the appropriate training and serving infrastructure

• What changes can be performed on statuses and transitions?

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

• Monitoring Privileged Account Access

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

• Target Hosts Using Privileged Session Manager

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

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

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

Who should take the CAU305 exam

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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How much CAU305 Exam Cost

The price of the CAU305 exam is $200 USD.