Connecting and preparing data, calculations and analysis, dashboards and stories, and publishing to Tableau Cloud and Server for the Tableau Certified Data Analyst exam.
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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.
Choose an extract over a live connection when offline access and fast in-memory querying matter more than real-time freshness. An extract materialises the source data into a local, compressed, columnar store that is queried in memory, so it serves analysis offline and reduces repeated load on a source that throttles live reads.
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.
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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.
Use replace data source to migrate existing worksheets to a structurally identical source without rebuilding them. Replace data source rebinds all worksheets, calculated fields, and dependencies from one connected source to another by matching field names, preserving the existing analysis when the schemas align.
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.
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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.
Choose a published data source to give many workbooks one curated, certified, secured model rather than duplicated per-workbook logic. A published data source stores the connection, joins, calculations, and access rules centrally on the server, so every workbook that connects to it inherits the same governed definitions instead of duplicating them.
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.
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