Tableau Certified Data Analyst (Analytics-DA-201) practice questions

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 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.