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CyberArk CAU305 Exam Reference
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.
Securely Managing the System Architecture
Choosing the appropriate training and serving infrastructure
ML APIs (e.g., Vision API, Speech API)
Which export options are available from the Issue Navigation? The following will be discussed here:
Use of edge compute
Identity and access management (e.g.,Cloud IAM)
Data security (encryption, key management)
What is an Issue Collector and what permissions are needed to configure it?
Ensuring scalability and efficiency
Resizing and autoscaling resources
How are valid JQL queries written? What operators and arguments are valid for various JQL functions (e.g. membersOf(), StartOfDay(), WAS, CHANGED, etc.)
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
Target Hosts Using Privileged Session Manager
Assessing, troubleshooting, and improving data representations and data processing infrastructure
Designing for security and compliance
Who can change the Simplified Workflow? What are the limitations on changes to the Simplified Workflow?
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
What features are available for Jira Software projects, and to users who have Jira Software application access (as compared to just Jira Core)?
Deploying an ML pipeline
When is it best to use components, and when is it best to use a custom field instead?
Choosing between ACID, idempotent, eventually consistent requirements
Which configurations (schemes, versions, components, etc) are shared versus unique when new projects are (1) created from template or (2) with a shared configuration?
Operationalizing machine learning models
Verification and monitoring
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 changes can be performed on statuses and transitions?
Data staging, cataloging, and discovery
Designing for data and application portability (e.g., multi-cloud, data residency requirements)
Hardware accelerators (e.g., GPU, TPU)
What are the uses of Project Category in Jira?
What is the relationship between screens, screen schemes and issue type screen schemes?
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Pipeline monitoring (e.g., Stackdriver)
Who can change a Project Key, and what are the effects on issues in the project?
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?
What is the importance of the Browse Projects permission?
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
What workflow changes can be done through the board by board administrators?
What types of workflow changes can Project Administrators make if Extended Project Administration is activated?
Measuring, monitoring, and troubleshooting machine learning models
Performing data preparation and quality control (e.g., Cloud Dataprep)
Defining the CyberArk Privileged Account Security Solution Architecture
Ingesting appropriate data
What are all the various factors that influence whether a field is visible on a screen?
How do combinations of permissions work together to enable specific actions?
What are the features, uses, limitations/restrictions of workflow changes, especially the Simplified Workflow?
Impact of dependencies of machine learning models
Ensuring reliability and fidelity
Which business requirements necessitate the use of separate issue types in a project (e.g. different screens, workflows, etc.)?
Mapping to current and future business requirements
Monitoring Privileged Account Access
Distributed vs. single machine
Ensuring privacy (e.g., Data Loss Prevention API)
Which project details can be added and changed by Project Administrators?
Conversational experiences (e.g., Dialogflow)
Leveraging pre-built ML models as a service
Ensuring flexibility and portability
Where are all the places you need to look to fully identify what permissions a particular user has?
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?
Building and running test suites
Continuous evaluation
How to Prepare for CyberArk CDE Recertification CAU305 Exam
Preparation Guide for CyberArk CDE Recertification CAU305 Exam
Introduction
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