Free Microsoft Certified: Power BI Data Analyst Associate (PL-300) Sample Questions

Correct answer: A — A dynamic M query parameter bound to a slicer, so the report-level filter value is passed into the Power Query source query at evaluation time.

• A. Dynamic M query parameters let a report-level filter or slicer selection be passed directly into a Power Query parameter used inside the DirectQuery source expression, so the resulting native query is filtered at the source rather than downloading unfiltered data.
• B. A calculated column on a DirectQuery table is evaluated in the source's native query language with real limitations, and concatenating category names does not push a viewer's runtime selection into the query; it does not achieve source-level filtering by itself.
• C. A hierarchy is a navigational grouping of columns for drill-down in visuals; it does not by itself cause a viewer's filter selection to fold into the DirectQuery source query the way a dynamic M parameter does.
• D. A bookmark captures the state of filters, slicers, and visual selections for later recall by the same or other users; it does not translate a live selection into a parameterized native query sent to the source.

Correct answer: A — Select the Dynamics 365 source in Data source settings, choose Edit Permissions, and enter new credentials under the credentials section.

• A. Data source settings lets an author select a listed source, open Edit Permissions, and supply new credentials for that connection, which is the direct and documented path for rotating the account used to authenticate.
• B. Deleting and retyping the query is unnecessary and risky, and a text comment naming an account has no effect on actual authentication; credentials are managed through the connection's stored settings, not through comments in M code.
• C. Privacy level is a data isolation classification used during query folding and has nothing to do with which account is used to authenticate; changing it does not substitute or update stored credentials.
• D. Gateway reinstallation is unrelated to rotating a Power BI Desktop data source's stored credentials for a cloud connector like Dynamics 365, and is not the documented method for updating credentials in this scenario.

Correct answer: B — False

• True. This is not the correct answer: documentation states that after a DirectQuery table's storage mode is set to Import or Dual, it generally can't be reversed back to DirectQuery, with only narrow exceptions in environments like Power BI web modeling or live editing that use version control to reverse the change.
• False. This is correct: switching a table from DirectQuery to Import is a one-way change in a standard file, so the claim that it can always be reversed with no restriction does not match documented behavior.

Correct answer: A — Unpivot the Jan through Dec columns into one Attribute column and one Value column holding month and revenue pairs

• A. Correct. Unpivoting the monthly columns produces one row per product-month combination with generic Attribute and Value columns, which is the long format a date-based visual needs.
• B. Incorrect. Pivoting would create even more wide columns keyed by product name, moving further away from the long format the scenario requires.
• C. Incorrect. Transposing swaps the entire table's row and column orientation rather than reshaping only the monthly columns into a long format.
• D. Incorrect. Concatenating the monthly columns into one string destroys the individual month and revenue values instead of splitting them into separate rows.

Correct answers: A, B, F — A referenced query re-executes the source query's steps at refresh time rather than storing a frozen, static copy of its output; Editing a step in the original query automatically changes the output of every query that references it; Duplicating a query copies the current steps, so future edits to the original will not affect the duplicate

• A. Correct. A referenced query is built by calling the original query as its source, so it re-executes those steps live at refresh time instead of storing a frozen, independent copy.
• B. Correct. Because a referenced query depends on the original's steps, any edit made to a step in the original propagates through and changes the output of every query built on top of it.
• C. Incorrect. The opposite is true: a referenced query stays dependent on the original rather than becoming independent, which is the entire mechanism that lets edits propagate.
• D. Incorrect. Duplicating a query does not prevent a later merge; a duplicated query is just a separate query that can still be merged with the original or any other query afterward.
• E. Incorrect. A referenced query is a normal query once created and can be merged with other queries just like any duplicated or freshly authored query.
• F. Correct. Duplicating freezes the current set of steps into a separate query, so later edits made to the original's steps have no effect on the already-duplicated copy.

Correct answers: A, B — Use "To Table" to convert the imported list of records into a table with one column of record values; Expand the resulting record column to promote each JSON field into its own column

• A. Correct. Converting an imported list of record values "To Table" is the standard first step, producing a single-column table where each row holds one order's record for further expansion.
• B. Correct. Expanding the record column is what promotes each JSON field, such as OrderID or Amount, into its own top-level column, delivering the tabular shape requested.
• C. Incorrect. Transposing swaps overall row and column orientation, which does not target the specific record column that actually needs expanding into fields.
• D. Incorrect. Grouping by the record column would collapse every order into aggregated summaries, losing the individual order rows the scenario needs to keep.
• E. Incorrect. Pivot Column spreads distinct values of one column into new column headers; a record column doesn't contain the kind of category values pivoting is designed to spread out.

Correct answer: A — Column distribution, because hovering over the distribution card reports both the distinct count and the unique count, where unique counts values that occur exactly once.

• A. Correct. Column distribution's hover card exposes both distinct count (the number of different values) and unique count (values that appear exactly one time), which is precisely the distinction the analyst needs.
• B. Incorrect. Column quality only classifies cells as Valid, Error, or Empty; it says nothing about how often a given value repeats across the column.
• C. Incorrect. The column profile statistics card reports measures like count, min, max, average, and standard deviation for numeric columns, not a distinct-versus-unique breakdown.
• D. Incorrect. Applied Steps is a list of transformation steps in the query, not a profiling tool, and it has no feature that surfaces per-value occurrence counts.

Correct answer: A — It is a cell-level error confined to that cell, so the query still loads and the rest of the table previews normally; a step-level error instead halts the whole query behind a yellow pane.

• A. Correct. A cell-level error keeps the query loading; only the offending cell shows the word Error, which can be inspected by selecting the cell's whitespace, unlike a step-level error that prevents the entire query from loading.
• B. Incorrect. This reverses the definitions: a step-level error is the one that blocks the entire query from loading and shows a yellow pane, while a cell-level error only affects the individual cell.
• C. Incorrect. Power Query does not label errors as source-level; the `NA` value converts successfully during import and only fails during the type-conversion step, which is a cell-level, not source, condition.
• D. Incorrect. A formula firewall error is specific to combining or merging queries with incompatible privacy levels and produces a distinct Formula.Firewall message, which does not match a single failed type conversion on one column.

Correct answer: A — Some `City` cells hold numbers rather than text, and `Text.Upper` does not convert them implicitly; the column quality bar shows how many cells are Error versus Empty.

• A. Correct. `Text.Upper` accepts text or null, but a numeric value is not converted to text automatically, so each numeric `City` cell becomes an Error cell. The column quality bar splits cells into Valid, Error and Empty, so it shows how many rows failed and how many are merely blank before the analyst converts the type or fixes the source values.
• B. Incorrect. `Text.Upper` is declared as taking and returning nullable text, so a null input simply returns null instead of an error. That is why the blank rows show null here; they do not need to be filtered out to avoid cell errors.
• C. Incorrect. Data privacy levels govern whether and how data from different sources can be combined or buffered; they do not block ordinary column transformations like uppercasing text within a single query.
• D. Incorrect. Power Query's M language uses square brackets to reference a column by name within row context; curly braces are used for list literals, not column references, so this would not be the correct syntax fix.

Correct answer: A — `REMOVEFILTERS ( 'Product'[Category] )`, clearing any existing filter on the Category column so the denominator always sums every category.

• A. Correct. REMOVEFILTERS clears any filter applied to the Category column, so wherever it is used inside CALCULATE the denominator always sums across every category regardless of what the report visual is currently filtered to.
• B. Incorrect. KEEPFILTERS adds a new filter condition while preserving existing ones on the same column; it locks the denominator to Bikes specifically rather than clearing the category filter to compute a grand total.
• C. Incorrect. USERELATIONSHIP switches which relationship is active between two tables for a calculation; it does not remove or modify any filter applied to the Category column.
• D. Incorrect. CROSSFILTER changes the direction filters propagate across a relationship; it does not clear the existing Category filter, so the denominator would still respect whatever category is selected.

Correct answer: A — `P90 Response = PERCENTILE.INC ( Calls[ResponseSeconds], 0.9 )`, returning the value at the 90th percentile of the response-time distribution.

• A. Correct. PERCENTILE.INC returns the value at the requested percentile of the distribution, so passing 0.9 gives exactly the response time under which 90 percent of calls fall, which is the service-level target the team wants.
• B. Incorrect. MEDIAN returns the 50th-percentile value, the midpoint of the distribution, not the 90th-percentile threshold the team specifically asked for.
• C. Incorrect. RANKX returns a rank position for a value relative to other rows, not a percentile threshold value; it answers a different question about ordering, not a cutoff response time.
• D. Incorrect. Multiplying the average by 0.9 is an arbitrary scaling of a mean value and has no statistical relationship to the 90th percentile of the actual distribution, especially with skewed outlier data.

Correct answers: A, B — `MEDIAN ( Transactions[Amount] )`, giving a typical transaction value that is not pulled upward by a small number of unusually large purchases.; `STDEV.P ( Transactions[Amount] )`, giving how tightly each department's transaction amounts cluster around their own average spend.

• A. Correct. MEDIAN returns the middle value of the distribution, which is resistant to a small number of extremely large one-off purchases pulling the typical figure upward, unlike an arithmetic mean.
• B. Correct. Standard deviation measures how spread out values are around their mean, which is exactly the volatility or clustering measure the team needs for comparing departments.
• C. Incorrect. SUM gives a total spend figure, not a typical per-transaction value or a measure of how spread out amounts are, so it does not address either stated need.
• D. Incorrect. COUNTROWS gives a simple transaction count, which says nothing about typical spend size or how volatile the transaction amounts are.
• E. Incorrect. MAX returns only the single largest value, which is itself an outlier and does not represent a typical spend or describe the spread of the full distribution.

Correct answer: A — Change one of the bidirectional relationships back to single-direction cross-filtering so only one unambiguous filter path remains between the tables.

• A. Reverting one bidirectional relationship to single direction removes the extra filter path that creates the ambiguity, which is exactly the documented approach for resolving ambiguous filter propagation.
• B. Changing cardinality to many-to-many alters how the relationship is evaluated but does not by itself remove the ambiguous path created by two other bidirectional relationships in the same chain.
• C. Renaming a key column does not change relationship cardinality or cross-filter direction and would not resolve an ambiguous filter path between three already-related tables.
• D. Marking a table as a date table only affects time intelligence and hierarchy behavior; it has no influence on how Power BI resolves ambiguous filter propagation paths.

Correct answer: A — The orphaned row's sales amount appears grouped under a blank virtual member, because table expansion adds a blank row on the "one" side for unmatched values.

• A. Table expansion for regular one-to-many relationships adds a blank virtual row on the "one" side for unmatched values, so the orphaned sale groups under a blank member rather than disappearing.
• B. Power BI does not discard rows that fail referential integrity by default; the sales amount still appears in totals and is grouped under the blank virtual member instead of being dropped.
• C. Model relationships do not enforce data integrity, so an unmatched ProductID does not block a data refresh; it only produces a blank virtual row at query time.
• D. Unmatched rows are grouped under a blank member representing the missing relationship, not reassigned to an arbitrary existing category based on alphabetical order.

Correct answer: A — The date column isn't contiguous from beginning to end, and it contains duplicate values, both of which fail Power BI's date table validation checks.

• A. Power BI's date table validation explicitly checks for a contiguous date range with no gaps and no duplicate values, and the missing leap day plus duplicate rows violate both checks directly.
• B. Data category is unrelated to date table validation, which checks contiguity, nulls, and uniqueness of the date column, not what category a column is assigned.
• C. Marking a table as a date table does not require any relationship to already exist; the validation runs against the column's own data, independent of relationships.
• D. Default Summarization affects how a numeric column aggregates on visuals and plays no role in the date table validation, which checks the date column's values, not its summarization setting.

Correct answers: A, B — Sorting a Month Name column by a numeric Month Number column so month labels display in calendar order on axes and slicers.; Sorting a Day Name column by a numeric Day of Week Number column so weekday labels display Monday through Sunday instead of alphabetically.

• A. Sorting Month Name by a numeric Month Number column is the canonical example of Sort by Column, producing calendar order instead of an alphabetical listing of month names.
• B. Sorting Day Name by a numeric day-of-week column follows the same pattern as month sorting and correctly produces a Monday-through-Sunday order instead of alphabetical order.
• C. Sort by Column must reference another column in the model, not a measure, and a measure's value changes with filter context, so it cannot be used as a static sort reference.
• D. There is no requirement to sort a column by itself before configuring Sort by Column on other columns in the same table; each column's setting is independent.
• E. Data category is a separate property from Sort by Column and controls how values are interpreted for features like maps, not the display order used for sorting.

Correct answer: A — Set the cross-filter direction on the relationship to single, and apply `CROSSFILTER` only in the specific measures that truly need bidirectional behavior.

• A. Correct — bidirectional filtering forces the engine to evaluate filter propagation in both directions for every query, which is both slower and a common source of ambiguous results; restricting it to single direction and applying `CROSSFILTER` only where needed narrows the cost to the reports that actually require it.
• B. Incorrect — inserting a bridging table adds another relationship to traverse without removing the bidirectional setting, so the ambiguity and the extra filter evaluation cost both remain.
• C. Incorrect — marking a table as a date table only enables time-intelligence functions on that table's date column; it has no relationship to cross-filter direction or query ambiguity.
• D. Incorrect — changing the relationship to many-to-many removes the guarantee of a unique key on either side, which typically makes filter propagation slower and more ambiguous, not less.

Correct answers: A, B, C — Add a filter step in Power Query that keeps only rows within the required 24-month window before the data loads into the model.; Set up incremental refresh with a defined refresh range that matches the 24 months of history the reports actually use.; Remove duplicate ticket rows caused by a known upstream export bug that occasionally writes the same ticket twice.

• A. Correct — filtering the source data down to the confirmed 24-month window before loading is the direct way to reduce row count while keeping the model aligned with what reports actually need.
• B. Correct — incremental refresh configured with a matching refresh range only stores and refreshes the rows inside that range, achieving the same reduction with an efficient, automated refresh pattern.
• C. Correct — removing genuine duplicate rows caused by a known data quality bug reduces row count without losing any real ticket, which is a legitimate and safe reduction.
• D. Incorrect — loading both an unfiltered backup copy and a filtered copy still loads the full 12 years of history into the model through the backup query, so total row count is not reduced.
• E. Incorrect — sorting rows changes their storage order but does not remove any of the 12 years of history, so the row count and model size stay the same.
• F. Incorrect — converting the date column to text does not remove any rows, and text storage of dates is generally larger and less efficient than the native date type, working against the goal.

Correct answer: A — True

• A. True — aggregating the source query to the month-and-store grain is precisely what reducing granularity means, and since no report needs finer detail, this cuts row count and improves performance without breaking any existing report.
• B. False would only apply if some report still needed transaction-line detail after the change, but since every report analyzes at month and store level, coarsening the grain to match does meet the stated requirement.

Correct answers: A, B, C — Allow end users to export summarized data shown on a visual, without exposing the underlying row-level records behind it.; Allow end users to export both summarized data and the underlying row-level data behind a visual.; Do not allow end users to export any data from visuals in the report.

• A. Correct: this is one of the three report-level export options, letting viewers take the aggregated values a visual displays while withholding the row-level detail.
• B. Correct: this is the broadest of the three options, permitting both the summarized values and the underlying transaction-level records behind them to be exported.
• C. Correct: this is the most restrictive of the three options, blocking any data export from the report's visuals regardless of aggregation level.
• D. Incorrect: there is no export option that restricts output specifically to a .pbix file; the report-level setting governs data export from visuals, not saving the report file itself.
• E. Incorrect: there is no image-only export mode among the report-level Export data choices; the three options all concern tabular data export at different levels of detail.

Correct answer: A — True

• A. Marking a personal bookmark as the default view causes that bookmarked filter state, including the region and date-range selections, to load automatically the next time that user opens the report, which satisfies the requirement.
• B. This is incorrect as a rejection of the solution: a personal bookmark marked as default does reliably restore its captured filters on reopen for the user who created it, so the proposed approach does meet the stated requirement.

Correct answer: A — Bin size

• A. Bin size is the specific control in the Groups dialog for a numeric field that determines the width of each equally sized bucket the values are grouped into.
• B. Default Summarization controls how a numeric column aggregates in a visual, such as sum or average, and is unrelated to defining the width of bins in the grouping dialog.
• C. Sort by Column controls the display order of one field relative to another and has no role in setting bin width when creating a numeric group.
• D. Data category tags a column's semantic type, such as marking it as a geographic address, and does not configure how a numeric field is divided into bins.

Correct answer: A — Visual calculations aren't supported on slicers, along with several other visual types such as R visuals and Key Influencers.

• A. Slicers are explicitly listed among the visual types, alongside R visuals, Python visuals, Key Influencers, and several others, that do not support visual calculations.
• B. Converting a slicer into a different visual type is not the documented reason or workaround; the restriction is that certain visual types categorically don't support visual calculations.
• C. Visual calculations work in both Power BI Desktop and the Power BI service, so the tool being used is not the reason the option is unavailable on a slicer.
• D. Visual calculations can reference columns and measures already on the visual, but they don't require a pre-existing model measure, so this is not the actual limitation.

Correct answers: A, B — Turn on Data labels in the Format pane and set their position above the columns.; Add the two extra fields to the visual's Tooltips field well so they appear on hover.

• A. Turning on Data labels and setting their position above the columns is exactly how labels showing each bar's value are added directly to a column chart.
• B. Adding fields to the Tooltips well is the documented way to surface extra fields on hover without adding them to the visual's main data area.
• C. The Analytics pane adds reference lines like averages to the visual itself and has no mechanism for surfacing additional fields inside a tooltip.
• D. The Sort axis property only changes the order columns appear in, it does not add data labels or populate the tooltip with extra fields.
• E. A Top N filter restricts which rows appear on the visual based on ranking, which has nothing to do with adding data labels or tooltip fields.

Correct answers: A, B, E — The workspace hosting the report must be on a paid Fabric capacity or Power BI Premium for the feature to be available.; Adding a narrative visual requires edit access to the workspace or report, or build access to the semantic model.; The narrative visual can be edited afterward, such as adjusting the generated text or adding data points.

• A. Copilot features, including generating a narrative visual, require the report's workspace to sit on a paid Fabric capacity or Power BI Premium, not just any license.
• B. Adding a narrative visual specifically requires edit access to the workspace or report, or build access to the underlying semantic model, per Copilot's access requirements.
• C. A narrative visual is an interactive object on the report, and its text can be regenerated or manually adjusted rather than being permanently locked at creation.
• D. A Pro license by itself is not sufficient for Copilot features; organizational Fabric capacity or Premium is also required.
• E. After Copilot generates a narrative visual, the author can continue editing it, such as refining wording or adding further data points, like any other visual.

Correct answer: A — Open the Selection pane's Tab order view, use Have tab order match visual order as a starting point, then fine-tune the sequence so it reads left to right, top to bottom.

• A. The Tab order view lets an author reorder keyboard focus directly, and starting from Have tab order match visual order followed by manual adjustment is the documented way to align it with a natural reading order.
• B. Object names in the Selection pane are labels only; renaming them does not change the sequence in which keyboard focus moves across visuals on the page.
• C. Layer order (z-order) controls which object appears on top when shapes overlap visually; it is a separate setting from tab order and does not control keyboard focus sequence.
• D. Alt text describes a visual's content to assistive technology; it has no mechanism for instructing focus to skip one object and jump to another, so it would not fix the tab sequence.

Correct answer: A — A decomposition tree with total shipping cost as the value and region, warehouse, and carrier added as explain-by fields, with AI splits enabled.

• A. A decomposition tree with AI splits enabled lets a user expand any branch in any order across the added dimensions, and the light-bulb AI split marks the dimension and value Power BI calculates as the highest or most unusual contributor at each step, matching every stated requirement.
• B. A matrix visual with nested rows enforces one fixed drill order defined by the row hierarchy and does not let the user branch differently per node or suggest which dimension to open next.
• C. Key influencers ranks factors associated with an outcome using statistical models; it does not provide the free-form, any-order drill-down interaction across a hierarchy that the manager described.
• D. Drillthrough navigates to a separate filtered page for one selected item and does not offer the flexible, branch-by-branch expansion through multiple dimensions on a single visual that the manager needs.

Correct answer: A — The Q&A visual, noting it is scheduled for deprecation, so the author should confirm this capability stays supported for the report's expected lifespan.

• A. The Q&A visual is specifically built to accept free-text natural language questions and generate an appropriate visualization in response, though it is scheduled for deprecation, which the author should factor into a decision about a report's expected lifespan.
• B. Smart narrative generates descriptive text summarizing an existing visual's data; it does not accept free-text questions and does not generate a new chart in response to typed input.
• C. Key influencers requires fields to be explicitly dragged into its Analyze and Explain by wells; it does not accept a free-text natural language question to automatically configure itself.
• D. A decomposition tree also requires fields to be dragged into its Analyze and Explain by wells; it does not interpret typed natural language questions to populate those wells.

Correct answer: A — The report automatically inherits the "Confidential" label from the semantic model, through inheritance upon creation.

• A. When new content is created in the Power BI service on top of a labeled semantic model or report, Power BI automatically applies the same sensitivity label to the new item, which is the documented inheritance-upon-creation behavior.
• B. Sensitivity labels apply to reports and dashboards as well as semantic models and dataflows, so reports are not excluded from labeling.
• C. Label inheritance happens automatically at creation time; and even if a label could not be applied for some reason, Power BI still allows the new item to be created rather than blocking it.
• D. A tenant default label policy can apply to unlabeled content, but when a parent semantic model already carries a label, downstream inheritance from that parent takes precedence over a generic tenant default.

Correct answer: A — Assign the users the Viewer workspace role and map them to the appropriate RLS role on the semantic model, since RLS is enforced for users with Viewer-level access.

• A. Viewer is the documented role to use for enforcing row-level security on users who browse content directly in a workspace; combined with mapping them to an RLS role on the semantic model, they see only their permitted rows while still being able to interact with reports.
• B. Contributor grants Build permission and content-editing rights, and content creators and editors are generally not the intended audience for enforced RLS in the same way Viewer-level consumers are; RLS is not conditioned on holding Contributor specifically.
• C. Admin grants full workspace control and is the opposite of a restricted-viewing role; assigning Admin to enforce RLS defeats the purpose of restricting what the users can see and manage.
• D. Users need some form of access to the report or workspace item to open it at all; RLS then restricts the data rows they see once they are viewing it, so a workspace role or item-level share is still required to reach the content.

Correct answer: B — False

• A. Adding users to a workspace role, even at a lower privilege level, is reserved for Admin and Member; Contributor can create and edit workspace content but cannot manage who else has access to the workspace.
• B. This is correct: only Admin and Member can add users to workspace roles. Contributor is limited to content creation, editing, and deletion, with no ability to manage membership at any privilege level.

Correct answer: A — The toggle adding the new report to the "Regional Managers" audience's included items

• A. Newly added workspace content is excluded from a published app by default, so each audience group's item list must be edited to include it before that audience can see it, even after Update app is selected.
• B. Audience members consume the app without needing a workspace role at all, so a workspace role assignment has no bearing on whether an item appears inside the published app.
• C. A missing refresh schedule would affect whether the report's data is current, not whether the report itself is visible in the app to a given audience group.
• D. A sensitivity label classifies and protects content but does not control which audience group inside an app can see a given item.

Correct answers: A, B, C — Semantic models; Reports; Apps

• A. Semantic models are one of the content types Power BI supports for endorsement, and are commonly endorsed since reports depend on them.
• B. Reports can be promoted or certified directly, independent of whether their underlying semantic model is also endorsed.
• C. Apps can carry an endorsement badge, which is useful when a broad audience is meant to find and trust the packaged content.
• D. Dashboards are not one of the content types Power BI endorsement currently supports.
• E. Paginated reports are not included in the set of content types that can be promoted or certified.
• F. A workspace itself is a container, not a content item, and is not something that carries a promoted or certified badge.

Correct answers: A, B, C — A Power BI Pro workspace allows up to eight scheduled refreshes per day for a semantic model; A workspace on Premium per user or Fabric capacity allows up to 48 scheduled refreshes per day; Power BI automatically deactivates a refresh schedule after four consecutive failed attempts

• A. Pro licensing caps a semantic model at eight scheduled refreshes per day, which is the documented Pro limit.
• B. Premium per user and Premium or Fabric (F SKU) capacity raise the limit to 48 scheduled refreshes per day for a semantic model.
• C. After four consecutive failures, Power BI deactivates the refresh schedule and the owner must resolve the underlying issue before turning it back on.
• D. Manual and API-triggered on-demand refreshes still count toward the same daily resource usage limits as scheduled refreshes, so they are not exempt.
• E. Power BI has no built-in monthly interval option on the schedule refresh screen; a monthly cadence requires an external tool such as Power Automate or a Fabric data pipeline.

Correct answer: A — True

• True. A data alert is event-driven and fires when a refresh causes the tracked value to cross the configured threshold, which is exactly the instant, condition-based notification requested instead of a scheduled snapshot.
• False. This is not the case, since a data alert is precisely the threshold-triggered mechanism the scenario is asking for, distinct from a subscription's fixed-cadence delivery.