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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.

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

• Defining the CyberArk Privileged Account Security Solution Architecture

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

• What is the importance of the Browse Projects permission?

• Data security (encryption, key management)

• What changes can be performed on statuses and transitions?

• Deploying an ML pipeline

• 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?

• Use of edge compute

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

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

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

• Impact of dependencies of machine learning models

• Measuring, monitoring, and troubleshooting machine learning models

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

• Data staging, cataloging, and discovery

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

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

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

• Ensuring flexibility and portability

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

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

• Conversational experiences (e.g., Dialogflow)

• Distributed vs. single machine

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

• Leveraging pre-built ML models as a service

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

• Designing for security and compliance

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

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

• Continuous evaluation

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

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

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

• Ensuring scalability and efficiency

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

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

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

• Mapping to current and future business requirements

• Pipeline monitoring (e.g., Stackdriver)

• Ingesting appropriate data

• Resizing and autoscaling resources

• Securely Managing the System Architecture

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

• What are the uses of Project Category in Jira?

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

• Building and running test suites

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

• Choosing between ACID, idempotent, eventually consistent requirements

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

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

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

• Ensuring reliability and fidelity

• Verification and monitoring

• Operationalizing machine learning models

• Target Hosts Using Privileged Session Manager

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

• Monitoring Privileged Account Access

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

• Choosing the appropriate training and serving infrastructure

• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))

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

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

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. Prep4sures 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.

What is the duration of the CAU305 Exam

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

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How to Prepare for CyberArk CDE Recertification CAU305 Exam

Preparation Guide for CyberArk CDE Recertification CAU305 Exam

Introduction

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