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