Tableau Updated SCA-C01 Exam Questions and Answers by niall

In Tableau Server, schedules manage tasks like extract refreshes and subscriptions. Each task within a schedule has a priority value (ranging from 1 to 100, where 1 is the highest priority and 100 is the lowest). Tasks with higher priority (lower numbers) are executed before tasks with lower priority (higher numbers) when queued by the Backgrounder process. If the Backgrounder is overloaded or delayed, lower-priority tasks may not complete on time, leading to inconsistent refreshes.

In this scenario:

The 7:00 AM Extract Refresh task is critical for the CEO’s workbook, but the data is only refreshed 70% of the time by 9:00 AM.

The server has a single node, meaning a single Backgrounder process handles all tasks. With five schedules (some overlapping in the early morning), contention or delays could prevent the 7:00 AM task from completing reliably before 9:00 AM.

Option C (Set the priority of this task to 1): Correct. Setting the task priority to 1 ensures it has the highest priority among all queued tasks. This increases the likelihood that the Backgrounder executes it promptly at 7:00 AM, completing the refresh before the CEO accesses the workbook at 9:00 AM. You can adjust task priority in the Tableau Server web interface under Schedules > Tasks > Edit Priority.

Option A (Set the default priority of this schedule to 50): Incorrect. The default priority for schedules is already 50, and this option refers to the schedule’s default, not the specific task. It wouldn’t address the contention issue.

Option B (Set the priority for all other tasks to 50): Incorrect. This keeps all tasks at the default priority (50), leaving the 7:00 AM task without a relative advantage. It doesn’t prioritize the CEO’s task.

Option D (Set the priority of this task to 100): Incorrect. Priority 100 is the lowest, which would deprioritize the task, making the refresh even less reliable.

Tableau Server uses caching to speed up workbook loading by storing query results. After a scheduled extract refresh updates the data, the cache may need recomputing—let’s dive into the mechanics:

Caching Basics:

VizQL Cache: Stores rendered views and query results for faster access.

Refresh Trigger: A scheduled refresh updates the underlying extract (.hyper), but the cache isn’t automatically invalidated—it’s demand-driven.

Recompute Conditions: Tableau recomputes the cache when the workbook is accessed (viewed) and its data has changed (e.g., via refresh).

Evaluation:

Option B (The workbook has upcoming scheduled refresh tasks): Correct.

Why: An upcoming refresh task indicates the workbook relies on an extract with a schedule. After the refresh runs, the data changes, priming the cache for recomputation on next view. Without a schedule, no refresh occurs, so this is a prerequisite.

Detail: Schedules are set inSchedules > Tasks—e.g., "Daily at 2 AM."

Option D (The workbook has been viewed recently): Correct.

Why: Viewing triggers cache recomputation if the data has changed (e.g., post-refresh). Tableau uses a "lazy caching" model—cache updates only when a user loads the workbook, ensuring fresh results.

Detail: "Recently" isn’t strictly defined but implies post-refresh access.

Option A (Published in the last month): Incorrect.

Why: Publish date is irrelevant—cache recomputation ties to data changes and access, not publication timing.

Option C (All Users group has permission rule allowing access): Incorrect.

Why: Permissions enable viewing, but recomputation requires actual access (viewing) and a refresh event, not just potential access.

Why This Matters: Caching balances performance and freshness—understanding triggers prevents stale data surprises.

Tableau Server relies on several processes to function, each with a specific role. TheRepositoryprocess (powered by PostgreSQL) serves as the database for metadata, storing critical information such as:

User and group details.

Permissions and site configurations.

Workbook and data source metadata (e.g., schedules, subscriptions).

Option B (Repository): Correct. The Repository is the centralized database that holds all metadata, making it the backbone of Tableau Server’s content management. There are typically two instances in an HA setup (one active, one passive), monitored by the Cluster Controller.

Option A (Data Engine): Incorrect. The Data Engine manages in-memory data processing and extract storage (e.g., .hyper files), not metadata. It’s separate from the Repository.

Option C (Backgrounder): Incorrect. The Backgrounder handles background tasks like extract refreshes and subscriptions, but it doesn’t store metadata—it interacts with the Repository to retrieve task details.

Option D (File Store): Incorrect. The File Store manages physical extract files and workbook assets, not metadata, which is stored in the Repository.

In Tableau Server, schedules manage tasks such as extract refreshes and subscriptions. The execution mode of a schedule determines how tasks within that schedule are processed by the Backgrounder process:

Parallel: Tasks run simultaneously (up to the Backgrounder’s capacity), which is the default setting.

Serial: Tasks run one-at-a-time in sequence, ensuring that one task completes before the next begins.

To ensure tasks associated with a particular schedule run one-at-a-time, you must configure the schedule’s execution mode toSerial. This is done in the Tableau Server web interface:

Go toSchedules.

Select the schedule, clickActions > Edit Schedule.

UnderExecution, chooseSerialinstead ofParallel.

Option A (Set Execution to Serial): Correct. This directly addresses the requirement by forcing tasks to execute sequentially.

Option B (Set Default priority to 0): Incorrect. Priority (1–100) determines the order of task execution across all schedules, not whether tasks run one-at-a-time within a single schedule. Also, 0 is not a valid priority value (minimum is 1).

Option C (Set Frequency to Hourly): Incorrect. Frequency (e.g., hourly, daily) controls when the schedule runs, not how tasks within it are executed.

Option D (Set Execution to Parallel): Incorrect. Parallel execution allows tasks to run simultaneously, which contradicts the requirement.