AI-generated skills test

Power BI test

Power BI has a specific trap for hiring managers: the reports look finished long before they are correct. A candidate can wire up a striking dashboard while the underlying model quietly double-counts, and a DAX measure can return a confident, precise, wrong number. A CV cannot tell you which candidate builds the model that reconciles and which one builds the one that silently misleads the finance team for a quarter. Only watching the work can.

A structured Power BI test does exactly that. Rather than trusting a badge, you see how a candidate designs a data model, writes a DAX measure that respects filter context, and judges whether a report answers the business question it claims to. Because H-Evaluate generates the assessment per job, a reporting-analyst role and a BI-developer role pull different scenarios from the real work. In the five-pillar framework this belongs to the Domain pillar, with questions AI-generated fresh for each role rather than reused from a shared bank.

The skill has grown more important, not less, as AI enters the workflow. Power BI now offers AI-generated measures and suggested visuals, and the analyst worth hiring is the one who can tell when a generated DAX expression is subtly wrong — miscounting because it ignores filter context, or aggregating at the wrong grain. The test observes that verifying judgement directly rather than inferring it from a title.

What it measures

Data modelling

Building a sound model — star schema over a flat table, clean relationships, avoiding the many-to-many and bidirectional-filter traps — so measures behave predictably. The foundation that determines whether every downstream number can be trusted.

DAX and calculation logic

Writing measures that respect filter and row context, using CALCULATE, iterators and time intelligence correctly. This is where confident, precise, wrong answers are born, so it is the sharpest signal of genuine competence.

Report and reporting judgement

Designing reports that answer the business question clearly and honestly — right visual, right grain, no misleading totals — and knowing when a request needs reframing before it is built.

Data preparation and correctness

Shaping data in Power Query and validating that a report reconciles: handling nulls and duplicates, checking totals against a known source, and refusing to ship a number they have not verified against reality.

Question formats

Scenario-based multiple choice on modelling, DAX and report designDAX tasks — write or correct a measure to return the right number in contextDebugging exercises — diagnose why a measure or model produces a wrong totalModelling tasks — structure relationships for a described reporting requirementShort written responses explaining a modelling trade-off or a reframed request

Who it's for

Use this test to screen Power BI developers, BI and reporting analysts, data analysts, and finance or operations analysts who own business reporting. It fits junior through mid-level hiring most directly, where the core uncertainty is whether a candidate can produce numbers the business can trust. For senior BI or analytics-engineer roles, run it as an early screen before deeper architecture and stakeholder interviews, and pair it with a case discussion on requirements gathering.

How to read the results

1Read the score as a band, not a decimal ranking — it identifies who can build a model and measures the business can rely on, and is worth interview time.
2Weight the DAX and modelling competencies heavily. A candidate who designs pretty reports but writes measures that ignore filter context is the exact risk this test exists to catch.
3Calibrate to seniority: expect clean measures from any analyst, but expect a senior candidate to also model defensively and reframe a flawed reporting request.
4Treat the result as one input alongside a structured interview, using any scenario where the report failed to reconcile as a concrete, evidence-based follow-up — never a single gate.

AI-generated skills test

Evaluate candidates on this skill with AI-generated questions

Configure a role-tuned assessment and watch it adapt by seniority — no signup.

Try the interactive demo View a sample assessment Explore the platform

Related roles

Data Analyst

Frequently asked questions

What does a Power BI test measure?

It measures whether a candidate can produce business reporting that is correct, not just attractive: designing a sound data model, writing DAX that respects filter context, preparing and validating data, and building reports that answer the question honestly. Rather than confirming a certification, it shows whether someone builds numbers the business can trust or ones that quietly mislead.

How do you assess Power BI and DAX skills?

The most reliable approach is job-shaped tasks: writing or correcting a DAX measure so it returns the right number in context, structuring relationships for a stated requirement, and diagnosing why a total is wrong. Combining these with report-design scenarios captures both the technical skill and the judgement to know a report is correct, which predicts performance far better than a certification or interview alone.

Why is DAX so important in a Power BI test?

DAX is where Power BI hides its hardest failures. A measure can look right and return a precise number that is wrong because it ignores filter or row context or aggregates at the wrong grain, and no one notices until the figures stop reconciling. Because that error is invisible in a finished report, testing DAX directly is the sharpest signal of whether a candidate genuinely understands the tool.

Are Power BI tests reliable for hiring?

A well-built Power BI test is reliable because every candidate faces comparable, job-relevant tasks under the same conditions, letting you compare demonstrated skill rather than certifications of uneven weight. Reliability improves when you read results as bands, weight the modelling and DAX competencies appropriately, and pair the score with a structured interview built around the tasks a candidate handled weakly.

Does the test cover data modelling, not just report building?

Yes, and modelling is treated as foundational. A weak model — a flat table where a star schema belongs, or careless bidirectional filters — makes every downstream measure unreliable no matter how polished the report. The test includes tasks on relationships and schema design precisely because a candidate who builds attractive visuals on a broken model is a costly and easily missed hiring mistake.