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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.

• Distributed vs. single machine

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

• Use of edge compute

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

• Securely Managing the System Architecture

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

• Ingesting appropriate data

• What is the importance of the Browse Projects permission?

• 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

• Leveraging pre-built ML models as a service

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

• Choosing the appropriate training and serving infrastructure

• Verification and monitoring

• Building and running test suites

• What are the uses of Project Category in Jira?

• Ensuring reliability and fidelity

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

• Target Hosts Using Privileged Session Manager

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

• Designing for security and compliance

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

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

• Pipeline monitoring (e.g., Stackdriver)

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

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

• Data security (encryption, key management)

• What changes can be performed on statuses and transitions?

• Defining the CyberArk Privileged Account Security Solution Architecture

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

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

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

• Conversational experiences (e.g., Dialogflow)

• Monitoring Privileged Account Access

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

• Mapping to current and future business requirements

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

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

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

• Impact of dependencies of machine learning models

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

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

• Resizing and autoscaling resources

• Continuous evaluation

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

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

• Choosing between ACID, idempotent, eventually consistent requirements

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

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

• Ensuring flexibility and portability

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

• Measuring, monitoring, and troubleshooting machine learning models

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

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

• Ensuring scalability and efficiency

• Deploying an ML pipeline

• Operationalizing machine learning models

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

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

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

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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. ActualTestsQuiz 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.

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Who should take the CAU305 exam

The CyberArk CDE certification is valid for 18 months. Certified Delivery Engineers will get automatic notifications 6 months and 3 months before their certification is expired. To get re-certified, CyberArk Certified Delivery Engineers should pass the CAU305: CyberArk CDE Recertification exam.

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