Elastic Analyst Practice Questions & Exam Guide

Elastic Certified Analyst is an analytics route for candidates who use Kibana to explore data, build dashboards, create visualizations, apply filters, use Lens, and communicate findings to stakeholders.

Use this page to try original IT Mastery sample questions on analysis decisions. They are not official Elastic exam questions.

What these questions test

• choosing data views, fields, filters, visualizations, and dashboard layouts for the question being answered
• using Discover, Lens, aggregations, drilldowns, and dashboard controls effectively
• interpreting trends without overclaiming from incomplete or poorly scoped data
• translating stakeholder questions into inspectable visual analysis

Sample Exam Questions

Question 1

Topic: data views

A user cannot find expected fields while building a Kibana visualization. What should be checked first?

• A. The dashboard background color
• B. The user’s browser bookmarks
• C. Whether the data view targets the right indices and has refreshed field information
• D. The cluster’s marketing name

Best answer: C

Explanation: Kibana visualizations rely on data views and field metadata. Wrong index targets or stale field lists can hide expected fields.

Question 2

Topic: Discover

What is Discover best suited for?

• A. Exploring individual documents, fields, filters, and time-scoped event data
• B. Editing index mappings directly
• C. Replacing all dashboards
• D. Changing node allocation settings

Best answer: A

Explanation: Discover helps analysts inspect documents and fields interactively. It is useful before turning a pattern into a visualization or dashboard.

Question 3

Topic: Lens

A business user wants a chart of orders by product category over time. What should the analyst configure?

• A. A cluster restart
• B. A snapshot repository
• C. A role with no permissions
• D. A Lens visualization with a time dimension and category breakdown using the relevant order fields

Best answer: D

Explanation: Lens is designed for drag-and-drop visual analysis. Time series plus category breakdown can answer the trend question.

Question 4

Topic: filters

Why should filters be clearly visible on a dashboard?

• A. Filters make all charts correct
• B. Users need to understand the scope of the data before interpreting visual results
• C. Hidden filters always improve trust
• D. Filters replace data quality checks

Best answer: B

Explanation: Dashboard viewers need context. Invisible or unclear filters can make correct charts misleading.

Question 5

Topic: aggregation choice

A chart needs the average response time by service. Which operation is most relevant?

• A. A document delete action
• B. A field rename in every source system
• C. A terms grouping by service with an average metric for response time
• D. A snapshot restore

Best answer: C

Explanation: Grouping by service and calculating average response time directly answers the analysis question.

Question 6

Topic: time picker

A dashboard appears to show no activity, but the data source is active. What should the analyst check?

• A. The selected time range and whether events fall inside it
• B. The dashboard title length
• C. The user’s monitor size
• D. The app icon

Best answer: A

Explanation: Kibana dashboards are commonly time-scoped. A narrow or wrong time picker can make valid data appear absent.

Question 7

Topic: drilldowns

Why add dashboard drilldowns?

• A. To hide the data source
• B. To delete the original visualization
• C. To disable all filters
• D. To let users move from a summary view into a filtered detail view or related dashboard

Best answer: D

Explanation: Drilldowns support investigation flow. They connect high-level signals to detailed context without rebuilding every search manually.

Question 8

Topic: misleading charts

A global average response-time chart hides severe latency in one region. What improvement helps?

• A. Remove the time range
• B. Break down the metric by region or add a regional filter/control
• C. Hide the labels
• D. Use only one color

Best answer: B

Explanation: Global averages can mask localized issues. Dimensions or filters expose segment-level behavior.

Question 9

Topic: dashboard design

What should a dashboard opening row usually provide?

• A. Every possible raw document field
• B. A list of unrelated screenshots
• C. High-level summary metrics that answer whether the system or business process needs attention
• D. No context

Best answer: C

Explanation: Dashboard viewers need fast orientation. Summary metrics should answer the first decision before deeper panels.

Question 10

Topic: saved objects

Why should dashboard and visualization ownership be clear?

• A. Owners help maintain definitions, permissions, changes, and trust in shared analysis assets
• B. Ownership prevents all data errors
• C. Ownership replaces access control
• D. Dashboards cannot be changed after creation

Best answer: A

Explanation: Shared visualizations need maintenance. Owners support review, permissions, and change management.

Question 11

Topic: stakeholder analysis

A stakeholder asks, “Are errors worse since yesterday’s release?” What should the analyst compare?

• A. Only the release name
• B. Dashboard color changes
• C. Index age only
• D. Error rate or count before and after the release, scoped to the relevant service, time windows, and traffic volume

Best answer: D

Explanation: The question requires a before/after comparison with relevant scope. Traffic volume matters because counts alone can mislead.

Question 12

Topic: data quality

A dashboard panel shows unexpected negative revenue. What is the best response?

• A. Publish it immediately with no context
• B. Verify source data, field type, transformations, filters, and aggregation logic before presenting the result as real
• C. Delete the entire dashboard
• D. Ignore stakeholder questions

Best answer: B

Explanation: Analysts should verify surprising results. Data quality, transformations, and aggregation design can all create misleading output.

Quick readiness checklist

Related IT Mastery pages

• Elastic Certified Engineer for Elasticsearch engineering scenarios
• Splunk Core User for search and dashboard basics
• CompTIA Data+ for data-analysis practice
• Tableau Data Analyst for analytics platform practice