Exam 70-778: Analyzing and Visualizing Data with Microsoft Power BI Practice Test
Exam 70-778 practice focuses on analyzing data and building interactive reports in Power BI. The available questions sample practical modeling and reporting decisions, including table relationships, date-table preparation, Power Query transformations, filter-responsive calculations, and ways to help report users inspect data behind visual summaries.
Exam overview
About the Exam 70-778: Analyzing and Visualizing Data with Microsoft Power BI Exam
Power BI analysis requires shaping source data, defining how tables relate, and choosing calculations and visuals that answer business questions. In a model with transaction-level facts and lookup tables, relationship cardinality and filter direction affect how dimensions such as customers, products, and dates influence results. Power Query tasks include expanding merged-table results and checking query folding to understand whether transformations can run at the data source. Report work also involves creating measures that respond to filters, enabling drill-through or inspection of contributing rows, identifying outliers, and presenting contextual explanations through tooltips. These skills support building models and reports that users can explore without obscuring how values were derived. The practice bank samples these Power BI concepts but does not establish official topic coverage or weighting.
Sample Practice Questions
Review a selection from this exam's published US Practice Tests practice bank.
A model contains a Sales table with a transaction date and a separate Calendar table with one row per date. You want calendar fields to filter sales in visuals. Which relationship is appropriate?
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Correct answer: AEach calendar date can correspond to many sales transactions, so the Calendar table is on the one side and Sales is on the many side. Relating the date fields allows calendar attributes to filter transaction data.
A model contains an Orders table with a CustomerKey and a Customers table with one row per customer. You want customer attributes to filter order-level visuals. Which relationship direction is generally appropriate for this model?
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Correct answer: BIn a typical star-schema relationship, the dimension table has one row per customer and filters the many-side Orders table. This lets customer attributes slice order data predictably.
A fact table contains one row per sale and a separate Products table contains one row per product. Which relationship best represents this design?
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Correct answer: BA product can appear in many sales rows, while each product key should identify one row in the Products table. A one-to-many relationship from Products to the sales fact table supports that structure and allows product attributes to filter sales.
A Power Query table has a column of nested tables produced by a merge. You want the matching fields from those nested tables included as columns in the current table. Which action should you use?
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Correct answer: AExpanding a nested-table column selects fields from the matched rows and adds them to the current table. Transposing changes row and column orientation, while the other actions do not extract fields from nested results.
You want to verify whether a Power Query transformation can be translated and executed by a relational data source instead of being performed locally. Which indicator is most relevant?
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Correct answer: BQuery folding describes whether Power Query can translate transformations into a source query for execution by the data source. Folding indicators or query-plan tools help assess this behavior.
A matrix visual has a numeric field in its Values well, and you want users to see the underlying rows that contribute to a selected summary cell. Which feature is designed for this purpose?
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Correct answer: BShow as a table presents the data behind the selected visual data point in tabular form. Drillthrough navigates to a report page with context filters, conditional formatting changes appearance, and expanding a hierarchy changes its displayed level.
You want to investigate whether a small set of unusually high transaction amounts is affecting a chart's scale. Which built-in visual capability can help identify and inspect such points?
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Correct answer: AAnomaly detection can identify points in a time-series visual that differ substantially from the expected pattern, helping an analyst inspect unusual values. Availability depends on the visual and configuration.
How to use this practice test
The free US Practice Tests session draws up to 20 random questions from the 50-question bank and displays results when you finish. Use the questions to check how you apply modeling, Power Query, and report-interaction concepts; review missed items by identifying the modeling or visual behavior that determines the answer.
Preparation tips
- Sketch fact and dimension tables, then check cardinality and filter direction against the analysis users need to perform.
- Practice expanding nested tables after a Power Query merge, and distinguish query folding from transformations performed locally.
- Create measures for filter-responsive aggregations and compare their results as report filters or slicers change.
- Mark a date table appropriately and test date-based filtering; also distinguish tooltips and drill-through from inspecting rows behind a summary value.
- Use visual-level exploration to investigate unusually large or small data points, then assess whether they distort the chart's scale.