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