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What is the duration of the CAU305 Exam

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

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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.

• Ensuring reliability and fidelity

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

• Choosing between ACID, idempotent, eventually consistent requirements

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

• Verification and monitoring

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

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

• What is the importance of the Browse Projects permission?

• Data security (encryption, key management)

• Defining the CyberArk Privileged Account Security Solution Architecture

• Pipeline monitoring (e.g., Stackdriver)

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

• Ingesting appropriate data

• Conversational experiences (e.g., Dialogflow)

• Measuring, monitoring, and troubleshooting machine learning models

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

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

• Ensuring flexibility and portability

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

• Continuous evaluation

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

• 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?

• Mapping to current and future business requirements

• Deploying an ML pipeline

• Resizing and autoscaling resources

• Ensuring scalability and efficiency

• Target Hosts Using Privileged Session Manager

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

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

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

• Securely Managing the System Architecture

• Monitoring Privileged Account Access

• Operationalizing machine learning models

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

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

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

• Data staging, cataloging, and discovery

• Leveraging pre-built ML models as a service

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

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

• Building and running test suites

• Use of edge compute

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

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

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

• What changes can be performed on statuses and transitions?

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

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

• Impact of dependencies of machine learning models

• What are the uses of Project Category in Jira?

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

• Distributed vs. single machine

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

• 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?

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

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

• Choosing the appropriate training and serving infrastructure

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

• Designing for security and compliance

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

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

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

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.

CyberArk CAU305 Exam Syllabus Topics: