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• Data security (encryption, key management)

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

• Building and running test suites

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

• Monitoring Privileged Account Access

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

• Use of edge compute

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

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

• Operationalizing machine learning models

• Where are all the places you need to look to fully identify what permissions a particular user has?

• Defining the CyberArk Privileged Account Security Solution Architecture

• Deploying an ML pipeline

• Which configurations (schemes, versions, components, etc) are shared versus unique when new projects are (1) created from template or (2) with a shared configuration?

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

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

• What changes can be performed on statuses and transitions?

• What features are available for Jira Software projects, and to users who have Jira Software application access (as compared to just Jira Core)?

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

• What is the relationship between screens, screen schemes and issue type screen schemes?

• Data staging, cataloging, and discovery

• Measuring, monitoring, and troubleshooting machine learning models

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

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

• Ensuring reliability and fidelity

• What is an Issue Collector and what permissions are needed to configure it?

• Leveraging pre-built ML models as a service

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

• Ingesting appropriate data

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

• What are the uses of Project Category in Jira?

• Verification and monitoring

• Mapping to current and future business requirements

• Conversational experiences (e.g., Dialogflow)

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

• Designing for security and compliance

• 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?

• Impact of dependencies of machine learning models

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

• Pipeline monitoring (e.g., Stackdriver)

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

• Ensuring scalability and efficiency

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

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

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

• Resizing and autoscaling resources

• Target Hosts Using Privileged Session Manager

• When is it best to use components, and when is it best to use a custom field instead?

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

• Ensuring flexibility and portability

• Distributed vs. single machine

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

• Securely Managing the System Architecture

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

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

• Continuous evaluation

• Choosing between ACID, idempotent, eventually consistent requirements

• Ensuring privacy (e.g., Data Loss Prevention API)

• Choosing the appropriate training and serving infrastructure

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

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