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Practice exam · Tableau

Tableau Desktop Specialist practice exam

45
questions
60
minutes
70%
to pass the practice exam
60
questions in the bank

Short answer

The Tableau Desktop Specialist practice exam has 45 questions in 60 minutes, with a 70% passing mark, as configured here. The fastest way to know whether you are ready is to sit a full exam on the clock and read your score by domain, not only the total.

Domains covered

  • Exploring and Analyzing Data
  • Connecting to and Preparing Data
  • Sharing Insights
  • Understanding Tableau Concepts

Sample questions, with explanations

Three questions from the bank, open to read.

Question 1 · Exploring and Analyzing Data

A workbook connects to a large SQL database. The analyst wants to permanently restrict the data so that only rows where Status = 'Active' are ever pulled into Tableau for every worksheet on this data source, reducing the amount of data retrieved from the database. Which filter type is the appropriate choice?

  1. A data source filter ✓ (correct)
  2. An extract that is later refreshed
  3. A context filter applied on every sheet
  4. A dimension filter added to each worksheet's Filters shelf
Why this is correct

A data source filter applies to the entire data source across all worksheets and is pushed to the database, limiting the rows retrieved — exactly the requirement here. A context filter only narrows data for the dependent filters on a single worksheet and must be set per sheet, so it does not provide a source-wide, all-sheets restriction. Creating an extract addresses storage and performance differently but, by itself, does not filter to Status = 'Active'; you would still need a filter (extract filter or data source filter) to limit the rows. Adding a dimension filter to each worksheet's shelf is both error-prone and sheet-specific — it does not guarantee the restriction everywhere and operates after data is already at the worksheet level rather than restricting what is retrieved for the whole source.

Question 2 · Exploring and Analyzing Data

An analyst wants to classify continuous Order Quantity values into ranges of width 5 (0-4, 5-9, 10-14, ...) to study how order size relates to discounting. Which Tableau feature is purpose-built to create these equal-width buckets from a continuous measure?

  1. Create a set on the Quantity field, which partitions values into in/out buckets of width 5.
  2. Create a group on the Quantity field, which combines selected values into custom equal-width buckets.
  3. Create a bin on the Quantity field with a size of 5, which automatically groups continuous values into equal-width intervals. ✓ (correct)
  4. Create a string parameter listing the ranges, which categorizes each Quantity into its band.
Why this is correct

Bins are the dedicated feature for converting a continuous numeric field into equal-width intervals; specifying a bin size of 5 yields the 0-4, 5-9, 10-14 buckets requested, so it is correct. 'Create a group' is wrong because groups merge specific selected members of a dimension into named combinations and do not generate evenly spaced numeric ranges automatically; you would have to hand-pick values. 'Create a set' is wrong because a set divides members into just two states (in or out) based on a condition, not into multiple equal-width numeric bands. 'Create a string parameter listing the ranges' is wrong because a parameter only stores a single chosen value and does no bucketing on its own; it cannot assign each Quantity to a band without an additional calculation, and even then it would not be the purpose-built bucketing tool.

Question 3 · Connecting to and Preparing Data

An analyst needs to combine sales figures stored in a Snowflake database with budget targets that exist only in a separate Google Sheets workbook. The two sources share a common 'Region' field. The granularity differs and the sources cannot be joined at the database level. Which technique is most appropriate?

  1. An inner join across the two sources in the physical layer using Region
  2. Pivot the Google Sheets data so it matches the Snowflake schema, then no combination is needed
  3. Data blending, using Region as the linking field between the primary and secondary sources ✓ (correct)
  4. A relationship in the logical layer connecting the two sources on Region
Why this is correct

Data blending is correct because blending is specifically designed to combine data from two separate, independent data sources (here Snowflake and Google Sheets) by matching on a common linking field like Region, aggregating the secondary source to the level of the primary. An inner join in the physical layer is a misconception; joins require the tables to be part of the same connection/source, and you cannot physically join a Snowflake table to a Google Sheet in the physical layer. A relationship also operates within a single data source's logical layer and cannot span two entirely different connections. Pivoting the Google Sheets data only reshapes one source; it does nothing to combine the two sources, so combination is still required.

Take the full practice exam

  • 45 questions drawn from the bank, with a 60-minute timer
  • Score by domain and a review of every question
  • An AI tutor to talk through each question you miss
  • What you miss feeds your study plan
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Independent, unofficial practice exam. Tableau and the exam name are trademarks of their owners; UpSkillScore is not affiliated with them. Format and passing mark are the exam's configuration in UpSkillScore, not an official statement.

Frequently asked questions

How many questions does the Tableau Desktop Specialist practice exam have?

It has 45 questions and a 60-minute limit. The passing mark of the practice exam is 70%.

Is this the official Tableau Desktop Specialist exam?

No. It is an independent practice exam built to match the structure of the real one. It does not replace the official exam.

Do I need an account to take it?

You can read the structure and sample questions without an account. Taking the full timed exam, scoring by domain and the AI tutor need one.