Elastic Certified Analyst Exam
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Certification for Kibana experts that have mastered data visualization and advanced analysis.
Course summary
The Elastic Certified Analyst exam tests your knowledge and skills in analyzing data using Kibana, including the ability to build visualizations and dashboards and detect anomalies of time-series data using machine learning.
To best prepare for this exam, we highly encourage participants to attend the Data Analysis with Kibana training course.
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- Topics
- Audience
- Duration
- Pre-reqs
- Requirements
Topics
To be fully prepared for the Elastic Certified Analyst exam, candidates should be able to complete all of the following exam objectives with only the assistance of the Elastic documentation:
- Define a data view with or without a Time Filter field
- Set the time filter to a specified date or time range
- Use Kibana Query Language (KQL) to display only documents that match a specified criteria
- Create and pin a filter based on a search criteria
- Apply a search criteria to a visualization or dashboard
Visualizing Data
- Create a visualization that displays pipeline aggregations (eg moving average) or custom formulas (eg filter ratio)
- Customize the format and colors of a Lens visualization
- Create maps satisfying a given criteria
- Create a table using Lens with columns, conditional coloring, and summary rows
- Create a Dashboard with visualization panels
- Define and use spaces in Kibana
- Create permalink to share dashboards
- Create interactive dashboards with input controls, text, and drilldowns
- Use Discover to save searches and add a document table to a dashboard
- Create Lens visualization with layers
- Create Lens visualization of a specified visualization type
- Add reference lines and annotations to a Lens visualization
Analyzing Data
- Answer questions about a given dataset using search and visualizations
- Use Machine Learning tools to detect anomalies in a dataset
- Define a single metric, multi-metric, population, or categorizationAnomaly Detection job
- Define and use runtime fields
- Define an alert using Kibana Alerts
- Create a data frame analytics job to detect outliers
- Create a transform that generates an entity-centric index
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