Free Analytics-DA-201 practice questions

A subscriptions dataset has a [Signup Date] and a [Cancel Date] field, both stored as dates. An analyst needs a calculated field that returns the whole number of complete months each customer stayed subscribed, so that a customer who signed up on 15 January and cancelled on 14 March counts as one month rather than two. Which calculation returns the correct result?

// Sample row:
// [Signup Date] = 2024-01-15
// [Cancel Date] = 2024-03-14
// Expected result = 1

• ADATEDIFF('month', [Cancel Date], [Signup Date]) - IIF(DAY([Cancel Date]) < DAY([Signup Date]), 1, 0)
• BDATEPART('month', [Cancel Date]) - DATEPART('month', [Signup Date]) - IIF(DAY([Cancel Date]) < DAY([Signup Date]), 1, 0)
• CDATEDIFF('month', [Signup Date], [Cancel Date]) + IIF(DAY([Cancel Date]) < DAY([Signup Date]), 1, 0)
• DDATEDIFF('month', [Signup Date], [Cancel Date]) - IIF(DAY([Cancel Date]) < DAY([Signup Date]), 1, 0)check_circle Correct

Why A is wrong: The day-adjustment is correct but the two date arguments are reversed, so DATEDIFF returns a negative value for any customer who cancels after signing up. The dates must run start then end.

Why B is wrong: Subtracting month numbers ignores the year, so it miscounts across year boundaries and returns a negative figure when the cancel month is numerically lower than the signup month.

Why C is wrong: Adding rather than subtracting one inflates the count further; DATEDIFF already over-counts when the end day is earlier, so the correction must reduce the total, not increase it.

Why D is correct: DATEDIFF with 'month' counts calendar-month boundaries crossed, so it over-counts by one when the end day has not yet reached the start day; subtracting one in that case yields complete elapsed months.

An analyst is building a discount-tier field from a numeric [Order Total] measure. Orders of 500 or more should read 'Gold', orders of 200 up to but not including 500 should read 'Silver', and everything below 200 should read 'Bronze'. They want the field to assign exactly one tier to every order. Which calculation produces the correct tiers?

// Boundaries:
// >= 500        -> 'Gold'
// 200 to < 500  -> 'Silver'
// < 200         -> 'Bronze'

• AIF [Order Total] >= 500 THEN 'Gold' ELSEIF [Order Total] >= 200 THEN 'Silver' ELSE 'Bronze' ENDcheck_circle Correct
• BCASE [Order Total] WHEN >= 500 THEN 'Gold' WHEN >= 200 THEN 'Silver' ELSE 'Bronze' END
• CIF [Order Total] >= 200 THEN 'Silver' ELSEIF [Order Total] >= 500 THEN 'Gold' ELSE 'Bronze' END
• DIF [Order Total] >= 500 THEN 'Gold' ELSEIF [Order Total] > 200 THEN 'Silver' ELSE 'Bronze' END

Why A is correct: IF and ELSEIF evaluate top to bottom and stop at the first true branch, so testing the highest boundary first and using >= for the 200 cut puts each order in exactly one tier including the inclusive 200 and 500 edges.

Why B is wrong: CASE compares a single expression against literal values with WHEN, so it cannot take comparison operators such as >= in its WHEN clauses; range logic of this kind requires IF and ELSEIF.

Why C is wrong: The branches are ordered wrongly: any order of 500 or more satisfies the first condition and is labelled 'Silver', so the 'Gold' branch is never reached. Overlapping IF tests must run from the most restrictive boundary downward.

Why D is wrong: Using a strict greater-than for the Silver boundary excludes an order of exactly 200, which the requirement places in 'Silver', so a value of 200 falls through to 'Bronze'. The boundary should be >=.

A [Customer ID] field arrived as a string such as '00482' with leading zeros preserved. An analyst wants a calculated field holding the value as a true integer so it can be summed and compared numerically, while ignoring the leading zeros. Which calculation returns an integer value?

// '00482' as text
// desired numeric value: 482

• AFLOAT([Customer ID])
• BINT([Customer ID])check_circle Correct
• CSTR([Customer ID])
• DROUND([Customer ID])

Why A is wrong: FLOAT does convert the text to a number, but it yields a decimal type such as 482.0 rather than a true integer, so it does not match a requirement for an integer field.

Why B is correct: INT converts a numeric string to a whole-number integer, dropping the leading zeros and producing 482, which can then be summed and compared numerically as required.

Why C is wrong: STR converts a value to text rather than to a number; the field is already a string, so this leaves it unchanged and still unusable for numeric aggregation.

Why D is wrong: ROUND expects a numeric argument and rounds decimal places; it does not perform the string-to-number type conversion needed here and would error on a text field.

A regional sales lead wants a single view that plots monthly revenue as columns and the running profit margin percentage as a line, with the two measures using independent vertical axes because their scales differ greatly. Which chart type meets this requirement?

• AA dual-axis combination chart with one measure as bars and the other as a linecheck_circle Correct
• BA stacked bar chart that layers both measures into a single set of columns
• CA pie chart split into slices for revenue and margin
• DA single-axis line chart drawing both measures against the same scale

Why A is correct: A dual-axis chart places two measures on separate, independently scaled vertical axes, and combining bar and line marks lets revenue and margin share one view despite their different ranges.

Why B is wrong: Stacking is tempting for combining two measures, but it forces both onto one shared axis and implies the parts sum to a total, which is wrong for a percentage and a currency value.

Why C is wrong: A pie chart shows parts of a whole at a single point in time and cannot display a monthly trend across two differently scaled measures.

Why D is wrong: Drawing both on one axis is the natural first attempt, but the percentage line would be flattened against the much larger revenue scale, defeating the comparison.

An analyst has a list of individual customer order values and wants to see how those values cluster into ranges such as 0 to 50, 50 to 100, and 100 to 150, with the height of each bar showing how many orders fall in each range. Which chart should the analyst build?

• AA pie chart with one slice per order value
• BA histogram built on binned order valuescheck_circle Correct
• CA scatter plot of order value against customer name
• DA standard bar chart with one bar per individual order

Why A is wrong: A pie chart would create one slice per order and show parts of a whole, not the frequency of values grouped into ranges.

Why B is correct: A histogram groups a continuous measure into equal-width bins and counts how many records fall in each, which is exactly the distribution of order values across ranges described.

Why C is wrong: A scatter plot shows the relationship between two measures as points and would not summarise how many orders fall within each value range.

Why D is wrong: A plain bar chart per order is a near miss because it also uses bars, but it shows each order separately rather than counting orders within grouped value ranges.

A quality team wants to compare the spread of defect counts across five production lines, showing the median, the upper and lower quartiles, and any outliers for each line side by side. Which chart type presents this five-number summary per category?

• AA line chart connecting the median defect count of each line
• BA tree map sized by total defect count per line
• CA box plot with one box per production linecheck_circle Correct
• DA clustered bar chart of average defect count per line

Why A is wrong: A line of medians is tempting because it uses the median, but it drops the quartiles and outliers and implies a sequence between unrelated production lines.

Why B is wrong: A tree map encodes a single value as rectangle size and cannot show median, quartiles, or outliers for each line.

Why C is correct: A box plot draws the median, the interquartile box, the whiskers, and individual outliers, so placing one box per line gives the side-by-side spread comparison requested.

Why D is wrong: A bar of averages summarises each line with one number and hides the spread, quartiles, and outliers that the team needs to compare.

A regional sales team works offline on long-haul flights and queries a 40-million-row table on a corporate database that throttles ad-hoc analytical reads during business hours. The team needs fast filtering and aggregation in the workbook without hitting the source repeatedly. Which connection approach best fits these constraints?

• ACreate an extract so the data is stored locally for offline use and fast in-memory querying that avoids repeated load on the throttled source.check_circle Correct
• BKeep a live connection so every interaction reflects the current state of the corporate database in real time.
• CUse a live connection but lower the workbook's refresh frequency so the database receives fewer queries during business hours.
• DKeep a live connection and rely on the source database's own result cache to satisfy offline interactions.

Why A is correct: An extract snapshots the data into a local columnar store, which works offline and serves fast filtering and aggregation from memory without querying the throttled source each time.

Why B is wrong: A live connection sends a query to the source on every interaction, which fails the offline requirement and worsens the throttling problem the team is trying to avoid.

Why C is wrong: There is no per-interaction refresh frequency to lower on a live connection; each view interaction still issues a query, so this does not solve offline access or throttling.

Why D is wrong: A source-side cache cannot serve a disconnected client; with no network the live connection has nothing to query, so offline analysis is impossible.

An analyst built a dashboard on a development copy of a database. The data has now moved to a production server with an identically structured table but a different host name and database name. The analyst wants every existing worksheet and calculated field on the dashboard to keep working against production. What is the most appropriate action?

• ADelete each worksheet and rebuild it from scratch against a fresh connection to the production server.
• BConnect to the production server as a new data source, then use replace data source to swap it in for the original so worksheets and fields remap by matching name.check_circle Correct
• CEdit only the live connection's server settings, because changing the host migrates every dependent worksheet automatically.
• DAdd the production server as a second data source and manually drag each field from the new source onto every view.

Why A is wrong: Rebuilding discards the existing layout and calculations unnecessarily; replacing the data source preserves the work when the structure matches, so a full rebuild is wasted effort.

Why B is correct: Replace data source remaps every dependent worksheet and calculated field from the old source to the new one by matching field names, which is exactly the structure-identical migration described.

Why C is wrong: Editing connection settings can repoint one connection, but it is the replace data source workflow that remaps worksheets and fields onto a separate source; relying on a host edit alone is the wrong mechanism for swapping in a distinct production source.

Why D is wrong: Manually re-dragging fields across a new source is laborious and error-prone; the replace data source feature does this remapping in one step when field names match.

A data governance lead wants one curated, certified version of the company's revenue model so that fifty analysts build workbooks on the same joins, calculations, and row-level security rather than each connecting directly to the warehouse with their own logic. Which connection target best meets this goal?

• AHave each analyst connect directly to the warehouse and agree informally to copy the same calculated fields between workbooks.
• BDistribute one packaged workbook file by email and ask everyone to build their analysis inside that single file.
• CConnect each workbook to a published data source on the server, so all analysts share the same curated model, calculations, and security rules.check_circle Correct
• DGive every analyst the warehouse credentials and a written standard describing the joins and calculations they should each recreate.

Why A is wrong: Direct warehouse connections with copied calculations drift apart over time and enforce no shared security, defeating the governed single-version goal.

Why B is wrong: A packaged workbook bundles a point-in-time copy for one author's analysis, not a reusable shared connection, so fifty analysts cannot all build governed workbooks from it.

Why C is correct: A published data source centralises the connection, joins, calculations, and security in one governed object that many workbooks reuse, which is precisely the single-source-of-truth requirement.

Why D is wrong: A written standard relies on each analyst recreating logic by hand, which reintroduces the inconsistency and unmanaged access the governance lead is trying to eliminate.

An analyst has built a workbook in Tableau Desktop that connects directly to a local Excel file saved on their laptop. They want to publish the workbook to Tableau Cloud so colleagues can view it without the analyst's laptop being switched on. What should the analyst do during publishing to make the data available to other users?

• APublish the workbook with the connection left as a live connection to the local Excel file path.
• BExport the workbook as a packaged PDF and email that file to each colleague instead.
• CPublish the workbook and rely on Tableau Cloud automatically uploading the Excel file from the laptop later.
• DInclude an extract of the Excel data in the published workbook so the data travels to Tableau Cloud.check_circle Correct

Why A is wrong: A live connection to a local file path is only reachable from the analyst's own machine, so colleagues on Tableau Cloud would see an error when the laptop is off; this is the very problem the analyst is trying to avoid.

Why B is wrong: A PDF export produces a static document rather than an interactive published workbook on Tableau Cloud, so it does not meet the goal of giving colleagues a live, viewable workbook on the site.

Why C is wrong: Tableau Cloud never reaches back into a user's laptop to fetch local files on its own, so this imagined automatic upload does not exist and the data would remain unavailable to colleagues.

Why D is correct: Including an extract embeds a snapshot of the Excel data inside the published workbook, so it lives on Tableau Cloud and remains available to colleagues regardless of whether the analyst's laptop is switched on.

A data engineer wants several different workbooks across their team to reuse one governed connection to the company's sales database, with shared field naming and calculations defined in a single place. Which type of content should the engineer publish to Tableau Server to achieve this?

• AA published data source, so multiple workbooks can connect to the same governed model.check_circle Correct
• BA separate packaged workbook for each report, each embedding its own copy of the connection.
• CA Prep flow that outputs the data, published so workbooks point at the flow directly.
• DA workbook saved as a template that colleagues copy and then repoint at the database.

Why A is correct: A published data source is the dedicated content type for sharing one governed connection, with its field names and calculations, that many separate workbooks can connect to and reuse.

Why B is wrong: Embedding a private copy of the connection in every workbook is tempting for simplicity, but it duplicates the model so there is no single shared place for naming and calculations to be governed.

Why C is wrong: A Prep flow shapes and outputs data on a schedule, but workbooks connect to a published data source or extract rather than pointing straight at a flow, so it is not the artefact that provides a shared connection model.

Why D is wrong: A copied template still leaves each colleague with their own independent connection definition, so calculations and field names drift apart instead of being governed centrally in one published object.

A reporting analyst needs to hand a single static page of a dashboard to a manager who only wants to print it and mark it up by hand, with no requirement for interactivity. From the dashboard open in Tableau Desktop, which export choice best fits this need?

• APublish the dashboard to Tableau Cloud and send the manager the view's web link.
• BExport the dashboard to PDF, producing a static page suitable for printing and annotation.check_circle Correct
• CExport the dashboard to a crosstab so the manager receives the underlying numbers in a spreadsheet.
• DSave the workbook as a packaged workbook file and send that for the manager to open.

Why A is wrong: A web link gives an interactive online view, which is fine in general but does not produce the static, print-ready single page the manager specifically asked to print and annotate by hand.

Why B is correct: Exporting to PDF renders the dashboard as a fixed, static page that prints cleanly and can be marked up by hand, matching the manager's stated need precisely.

Why C is wrong: A crosstab export delivers tabular data in a spreadsheet rather than the laid-out dashboard page, so it loses the visual layout the manager wants to print and mark up.

Why D is wrong: A packaged workbook requires Tableau software to open and is interactive rather than a print-ready page, so it does not give the manager the simple static document they asked for.