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How to study the CAU305 Exam

Recertifications are an important part of maintaining credentials and status of the candidate. Practice exams and knowledge exam dumps are built to make students comfortable with the real exam environment. Pass4Leader expert team recommends you to prepare notes on these topics as you go along preparing with it and don't forget to practice CAU-305 practice exams and revise CAU-305 exam dumps.

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.

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

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

• Target Hosts Using Privileged Session Manager

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

• What is the importance of the Browse Projects permission?

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

• Ensuring reliability and fidelity

• Choosing the appropriate training and serving infrastructure

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

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

• Mapping to current and future business requirements

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

• Verification and monitoring

• Impact of dependencies of machine learning models

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

• Building and running test suites

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

• Securely Managing the System Architecture

• Defining the CyberArk Privileged Account Security Solution Architecture

• Continuous evaluation

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

• Leveraging pre-built ML models as a service

• 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?

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

• Monitoring Privileged Account Access

• Data security (encryption, key management)

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

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

• Data staging, cataloging, and discovery

• Distributed vs. single machine

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

• Resizing and autoscaling resources

• Designing for security and compliance

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

• Conversational experiences (e.g., Dialogflow)

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

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

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

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

• 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?

• 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?

• Pipeline monitoring (e.g., Stackdriver)

• Choosing between ACID, idempotent, eventually consistent requirements

• Deploying an ML pipeline

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

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

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

• Operationalizing machine learning models

• Ensuring scalability and efficiency

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

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

• Ensuring flexibility and portability

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

• Ingesting appropriate data

• Use of edge compute

• What are the uses of Project Category in Jira?

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

• What changes can be performed on statuses and transitions?

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

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

What is the duration of the CAU305 Exam

• Number of Questions: 40
• Length of Examination: 90 minutes
• Passing Score: 70%
• Format: Multiple choices, multiple answers

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Certification Path

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