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.

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

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

• Ingesting appropriate data

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

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

• 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?

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

• Use of edge compute

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

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

• Securely Managing the System Architecture

• Data staging, cataloging, and discovery

• Data security (encryption, key management)

• Resizing and autoscaling resources

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

• 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?

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

• Impact of dependencies of machine learning models

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

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

• Target Hosts Using Privileged Session Manager

• 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?

• 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 security and compliance

• Verification and monitoring

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

• Conversational experiences (e.g., Dialogflow)

• What is the importance of the Browse Projects permission?

• What changes can be performed on statuses and transitions?

• 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?

• Pipeline monitoring (e.g., Stackdriver)

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

• Distributed vs. single machine

• Measuring, monitoring, and troubleshooting machine learning models

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

• Mapping to current and future business requirements

• Leveraging pre-built ML models as a service

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

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

• Choosing the appropriate training and serving infrastructure

• Ensuring flexibility and portability

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

• Defining the CyberArk Privileged Account Security Solution Architecture

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

• Deploying an ML pipeline

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

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

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

• Ensuring scalability and efficiency

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

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

• Ensuring reliability and fidelity

• What are the uses of Project Category in Jira?

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

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

• Continuous evaluation

• Monitoring Privileged Account Access

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

• Choosing between ACID, idempotent, eventually consistent requirements

Difficulty in writing CAU305 Exam

Candidates already have experience with Sentry and Defender examination so it is not difficult to pass the recertifications. CAU-305 exam dumps and CAU-305 practice exams to revise the already discussed syllabus.

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