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CyberArk CAU-401 Exam Reference

Reference: https://training.cyberark.com/instructor-led-training/cyberark-end-point-manager

CAU-401 Exam topics

Candidates must know the EPM topics before they start of preparation because it will really help them solving the problems. Our CAU-401 practice test and CAU-305 practice exams will include the following topics. These are covered in our CAU-305 exam dumps.

• What things may need to be updated as a result of workflow changes of any kind?
• Retrieve logs and perform simple troubleshooting tasks
• Choosing between ACID, idempotent, eventually consistent requirements
• What is the importance of the Browse Projects permission?
• Configure ransomware and threat protection
• Target Hosts Using Privileged Session Manager
• Identity and access management (e.g.,Cloud IAM)
• Operationalizing machine learning models
• Monitoring Privileged Account Access
• Choosing the appropriate training and serving infrastructure
• What changes can be performed on statuses and transitions?
• Measuring, monitoring, and troubleshooting machine learning models
• Data staging, cataloging, and discovery
• What is an Issue Collector and what permissions are needed to configure it?
• Defining the CyberArk Privileged Account Security Solution Architecture
• Impact of dependencies of machine learning models
• What is the relationship between screens, screen schemes and issue type screen schemes?
• Building and running test suites
• Common sources of error (e.g., assumptions about data)
• When is it best to use components, and when is it best to use a custom field instead?
• What types of workflow changes can Project Administrators make if Extended Project Administration is activated?
• How are valid JQL queries written? What operators and arguments are valid for various JQL functions (e.g. membersOf(), StartOfDay(), WAS, CHANGED, etc.)
• What workflow changes can be done through the board by board administrators?
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Use of edge compute
• Ensuring privacy (e.g., Data Loss Prevention API)
• Who can change a Project Key, and what are the effects on issues in the project?
• Data security (encryption, key management)
• 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?
• Where are all the places you need to look to fully identify what permissions a particular user has?
• Who can change the Simplified Workflow? What are the limitations on changes to the Simplified Workflow?
• Ensuring reliability and fidelity
• Pipeline monitoring (e.g., Stackdriver)
• Continuous evaluation
• What features are available for Jira Software projects, and to users who have Jira Software application access (as compared to just Jira Core)?
• Leveraging pre-built ML models as a service
• Conversational experiences (e.g., Dialogflow)
• What are all the various factors that influence whether a field is visible on a screen?
• What are the uses of Project Category in Jira?
• Ensuring scalability and efficiency
• ML APIs (e.g., Vision API, Speech API)
• Ingesting appropriate data
• Which project details can be added and changed by Project Administrators?
• What are the features, uses, limitations/restrictions of workflow changes, especially the Simplified Workflow?
• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
• Describe implementation and roll-out best practices
• Resizing and autoscaling resources
• Hardware accelerators (e.g., GPU, TPU)
• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
• Which business requirements necessitate the use of separate issue types in a project (e.g. different screens, workflows, etc.)?
• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Designing for data and application portability (e.g., multi-cloud, data residency requirements)
• Describe the EPM solution and architecture
• Ensuring flexibility and portability
• Verification and monitoring
• Distributed vs. single machine
• Mapping to current and future business requirements
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• Securely Managing the System Architecture
• How do combinations of permissions work together to enable specific actions?
• Deploy and upgrade the EPM Agent
• Which configurations (schemes, versions, components, etc) are shared versus unique when new projects are (1) created from template or (2) with a shared configuration?
• Performing data preparation and quality control (e.g., Cloud Dataprep)
• Deploying an ML pipeline
• Which export options are available from the Issue Navigation?
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• What are the use cases where it is best to use Project Roles rather than Groups?
• Designing for security and compliance

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CyberArk CAU401 Exam Syllabus Topics: