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CAU305 Exam topics

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• Leveraging pre-built ML models as a service

• 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 relationship between screens, screen schemes and issue type screen schemes?

• Deploying an ML pipeline

• Verification and monitoring

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

• Distributed vs. single machine

• What are the uses of Project Category in Jira?

• Ensuring reliability and fidelity

• Measuring, monitoring, and troubleshooting machine learning models

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

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

• Who can change a Project Key, and what are the effects on issues in the project?

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

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

• Data staging, cataloging, and discovery

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

• Ensuring flexibility and portability

• Impact of dependencies of machine learning models

• Ingesting appropriate data

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

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

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

• What is the importance of the Browse Projects permission?

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

• Securely Managing the System Architecture

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

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

• 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?

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

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

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

• Choosing the appropriate training and serving infrastructure

• Conversational experiences (e.g., Dialogflow)

• Designing for security and compliance

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

• Ensuring scalability and efficiency

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

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

• Resizing and autoscaling resources

• Operationalizing 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?

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

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

• Mapping to current and future business requirements

• Building and running test suites

• 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?

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

• Which export options are available from the Issue Navigation? The following will be discussed here:

• What changes can be performed on statuses and transitions?

• Monitoring Privileged Account Access

• Defining the CyberArk Privileged Account Security Solution Architecture

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

• Target Hosts Using Privileged Session Manager

• Data security (encryption, key management)

• Use of edge compute

• Pipeline monitoring (e.g., Stackdriver)

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

• Continuous evaluation

CyberArk CAU305 exam is a certification exam that focuses on the CyberArk Certified Delivery Engineer (CDE) recertification. CAU305 exam is designed to test the knowledge and skills of professionals who are responsible for deploying and managing CyberArk solutions in their organizations. CyberArk is a leading provider of privileged access management (PAM) solutions, and the CAU305 exam is an important step in maintaining the currency of your CyberArk CDE certification.

CyberArk CDE certification is highly respected in the industry and demonstrates a deep understanding of privileged account security and management. It is a vendor-neutral certification, meaning that it is not tied to any specific technology or product. This makes it an ideal choice for security professionals who want to develop their skills and knowledge in this area. CyberArk CDE Recertification certification is recognized globally and can lead to exciting career opportunities in the field of cybersecurity.

CyberArk CAU305 Exam Syllabus Topics: