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- Step 1: Visit to Pearson VUE Exam Registration
- Step 2: Signup/Login to Pearson VUE account
- Step 3: Search for CyberArk CAU305 Exam Certifications Exam
- Step 4: Select Date, time and confirm with payment method
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Data staging, cataloging, and discovery
Who can change a Project Key, and what are the effects on issues in the project?
What features are available for Jira Software projects, and to users who have Jira Software application access (as compared to just Jira Core)?
Building and running test suites
Operationalizing machine learning models
Monitoring Privileged Account Access
Assessing, troubleshooting, and improving data representations and data processing infrastructure
Distributed vs. single machine
Which business requirements necessitate the use of separate issue types in a project (e.g. different screens, workflows, etc.)?
What is the importance of the Browse Projects permission?
Designing for security and compliance
How are valid JQL queries written? What operators and arguments are valid for various JQL functions (e.g. membersOf(), StartOfDay(), WAS, CHANGED, etc.)
Choosing between ACID, idempotent, eventually consistent requirements
ML APIs (e.g., Vision API, Speech API)
Ensuring privacy (e.g., Data Loss Prevention API)
What are the features, uses, limitations/restrictions of workflow changes, especially the Simplified Workflow?
Securely Managing the System Architecture
Mapping to current and future business requirements
Resizing and autoscaling resources
Ingesting appropriate data
Data security (encryption, key management)
Which export options are available from the Issue Navigation? The following will be discussed here:
What workflow changes can be done through the board by board administrators?
Where are all the places you need to look to fully identify what permissions a particular user has?
What types of workflow changes can Project Administrators make if Extended Project Administration is activated?
Hardware accelerators (e.g., GPU, TPU)
Designing for data and application portability (e.g., multi-cloud, data residency requirements)
Ensuring scalability and efficiency
Use of edge compute
Who can change the Simplified Workflow? What are the limitations on changes to the Simplified Workflow?
Which configurations (schemes, versions, components, etc) are shared versus unique when new projects are (1) created from template or (2) with a shared configuration?
Ensuring reliability and fidelity
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Leveraging pre-built ML models as a service
Conversational experiences (e.g., Dialogflow)
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
Deploying an ML pipeline
Continuous evaluation
Common sources of error (e.g., assumptions about data)
What are the uses of Project Category in Jira?
What things may need to be updated as a result of workflow changes of any kind?
Pipeline monitoring (e.g., Stackdriver)
Verification and monitoring
What is the relationship between screens, screen schemes and issue type screen schemes?
What is an Issue Collector and what permissions are needed to configure it?
Defining the CyberArk Privileged Account Security Solution Architecture
Target Hosts Using Privileged Session Manager
What are the use cases where it is best to use Project Roles rather than Groups?
Which project details can be added and changed by Project Administrators?
What are all the various factors that influence whether a field is visible on a screen?
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
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?
Performing data preparation and quality control (e.g., Cloud Dataprep)
Choosing the appropriate training and serving infrastructure
When is it best to use components, and when is it best to use a custom field instead?
Ensuring flexibility and portability
Measuring, monitoring, and troubleshooting machine learning models
Impact of dependencies of machine learning models
Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
Identity and access management (e.g.,Cloud IAM)
What changes can be performed on statuses and transitions?
How do combinations of permissions work together to enable specific actions?
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
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