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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.
Operationalizing machine learning models
Use of edge compute
Securely Managing the System Architecture
Distributed vs. single machine
Measuring, monitoring, and troubleshooting machine learning models
Deploying an ML pipeline
Defining the CyberArk Privileged Account Security Solution Architecture
When is it best to use components, and when is it best to use a custom field instead?
Ensuring privacy (e.g., Data Loss Prevention API)
Where are all the places you need to look to fully identify what permissions a particular user has?
Ensuring reliability and fidelity
Pipeline monitoring (e.g., Stackdriver)
What are the features, uses, limitations/restrictions of workflow changes, especially the Simplified Workflow?
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?
What features are available for Jira Software projects, and to users who have Jira Software application access (as compared to just Jira Core)?
Data staging, cataloging, and discovery
Continuous evaluation
Which project details can be added and changed by Project Administrators?
What is an Issue Collector and what permissions are needed to configure it?
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.)
Target Hosts Using Privileged Session Manager
What is the relationship between screens, screen schemes and issue type screen schemes?
ML APIs (e.g., Vision API, Speech API)
Data security (encryption, key management)
What are the use cases where it is best to use Project Roles rather than Groups?
How do combinations of permissions work together to enable specific actions?
Common sources of error (e.g., assumptions about data)
Which business requirements necessitate the use of separate issue types in a project (e.g. different screens, workflows, etc.)?
Performing data preparation and quality control (e.g., Cloud Dataprep)
What things may need to be updated as a result of workflow changes of any kind?
Ensuring flexibility and portability
Designing for security and compliance
Which export options are available from the Issue Navigation? The following will be discussed here:
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Impact of dependencies of machine learning models
What types of workflow changes can Project Administrators make if Extended Project Administration is activated?
Choosing the appropriate training and serving infrastructure
Designing for data and application portability (e.g., multi-cloud, data residency requirements)
Assessing, troubleshooting, and improving data representations and data processing infrastructure
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Monitoring Privileged Account Access
Leveraging pre-built ML models as a service
Mapping to current and future business requirements
Ensuring scalability and efficiency
Choosing between ACID, idempotent, eventually consistent requirements
Which configurations (schemes, versions, components, etc) are shared versus unique when new projects are (1) created from template or (2) with a shared configuration?
Verification and monitoring
Building and running test suites
Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
What are all the various factors that influence whether a field is visible on a screen?
Resizing and autoscaling resources
Who can change the Simplified Workflow? What are the limitations on changes to the Simplified Workflow?
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
What workflow changes can be done through the board by board administrators?
What changes can be performed on statuses and transitions?
Who can change a Project Key, and what are the effects on issues in the project?
What are the uses of Project Category in Jira?
Ingesting appropriate data
Hardware accelerators (e.g., GPU, TPU)
What is the importance of the Browse Projects permission?
Conversational experiences (e.g., Dialogflow)
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
Representative types of CAU305日本語 study material
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Valid contents of CAU305日本語 exam study material
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No help, Full refund!
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