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.
Fast learning
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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. CramPDF 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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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.
ML APIs (e.g., Vision API, Speech API)
Use of edge compute
Continuous evaluation
Who can change the Simplified Workflow? What are the limitations on changes to the Simplified Workflow?
Mapping to current and future business requirements
What is the importance of the Browse Projects permission?
Resizing and autoscaling resources
Data staging, cataloging, and discovery
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.)
What types of workflow changes can Project Administrators make if Extended Project Administration is activated?
Ensuring flexibility and portability
What things may need to be updated as a result of workflow changes of any kind?
What changes can be performed on statuses and transitions?
What is an Issue Collector and what permissions are needed to configure it?
Ensuring privacy (e.g., Data Loss Prevention API)
Verification and monitoring
What are the use cases where it is best to use Project Roles rather than Groups?
Defining the CyberArk Privileged Account Security Solution Architecture
Distributed vs. single machine
Which business requirements necessitate the use of separate issue types in a project (e.g. different screens, workflows, etc.)?
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
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 configuration options for all the out-of-box reports and gadgets (except Agile reports and gadgets)? What is their behavior and expected results?
Assessing, troubleshooting, and improving data representations and data processing infrastructure
Data security (encryption, key management)
Ensuring scalability and efficiency
What workflow changes can be done through the board by board administrators?
What is the relationship between screens, screen schemes and issue type screen schemes?
Which export options are available from the Issue Navigation? The following will be discussed here:
Conversational experiences (e.g., Dialogflow)
Which configurations (schemes, versions, components, etc) are shared versus unique when new projects are (1) created from template or (2) with a shared configuration?
Hardware accelerators (e.g., GPU, TPU)
Building and running test suites
Pipeline monitoring (e.g., Stackdriver)
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Leveraging pre-built ML models as a service
Measuring, monitoring, and troubleshooting machine learning models
Securely Managing the System Architecture
Designing for security and compliance
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Impact of dependencies of machine learning models
What features are available for Jira Software projects, and to users who have Jira Software application access (as compared to just Jira Core)?
When is it best to use components, and when is it best to use a custom field instead?
Ingesting appropriate data
Performing data preparation and quality control (e.g., Cloud Dataprep)
Monitoring Privileged Account Access
Designing for data and application portability (e.g., multi-cloud, data residency requirements)
What are all the various factors that influence whether a field is visible on a screen?
Target Hosts Using Privileged Session Manager
Which project details can be added and changed by Project Administrators?
Where are all the places you need to look to fully identify what permissions a particular user has?
Common sources of error (e.g., assumptions about data)
Deploying an ML pipeline
What are the uses of Project Category in Jira?
Who can change a Project Key, and what are the effects on issues in the project?
Ensuring reliability and fidelity
Operationalizing machine learning models
How do combinations of permissions work together to enable specific actions?
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
Choosing between ACID, idempotent, eventually consistent requirements
What are the features, uses, limitations/restrictions of workflow changes, especially the Simplified Workflow?
Choosing the appropriate training and serving infrastructure
High pass rate
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How much CAU305 Exam Cost
The price of the CAU305 exam is $200 USD.
