Pass the Salesforce Administrator Analytics-Admn-201 Questions and answers with ValidTests

Tableau Server provides multiple methods to create groups (collections of users) and projects (content containers), catering to UI, CLI, and programmatic needs. Let’s dissect each option with depth:

Option B (Tableau Server browser interface): Correct.

Groups: Go to Users > Groups > Add Group, name it, and optionally sync with Active Directory.

Projects: Go to Content > Projects > New Project, set name, description, and permissions.

Details: The web UI is intuitive, requiring server/site administrator rights. It’s ideal for manual, ad-hoc creation with immediate visibility.

Permissions: For projects, you can set default permissions or lock them here.

Option C (tabcmd): Correct.

Groups: tabcmd creategroup "GroupName" creates a local group. Add users with tabcmd addusers "GroupName" --users "user1,user2".

Projects: tabcmd createproject -n "ProjectName" -d "Description" creates a project.

Details: tabcmd is a command-line tool for batch operations or scripting (e.g., automating group/project setup). It requires a server admin login (tabcmd login).

Limitation: No AD sync via tabcmd—that’s UI or REST API territory.

Option D (REST API): Correct.

Groups: Use the POST /api/api-version/sites/site-id/groups endpoint with a payload (e.g., {"group": {"name": "GroupName"}}). Supports AD import too.

Projects: Use POST /api/api-version/sites/site-id/projects (e.g., {"project": {"name": "ProjectName", "description": "Desc"}}).

Details: The REST API is programmatic, ideal for integration with external systems or bulk automation. Requires authentication via a token and server/site admin rights.

Power: Offers full control, including nested projects and custom permissions.

Option A (tsm customize): Incorrect.

Purpose: tsm customize modifies TSM UI branding (e.g., logos, colors) via commands like tsm customize --logo "path/to/logo.png".

Why Wrong: It’s unrelated to creating groups or projects—it’s for cosmetic server configuration, not content/user management.

Why This Matters: Offering UI, CLI, and API options ensures flexibility—manual for small tasks, automation for scale—critical in enterprise deployments.

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 in Schedules > 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.

In a high-availability (HA) Tableau Server setup, the Repository (PostgreSQL) has an active and passive instance. Failover occurs if the active Repository fails. Let’s dive into the process:

HA Setup:

Two Repository instances across nodes (active/passive).

Failover switches to the passive instance if the active one becomes unavailable (e.g., crash, network issue).

Cluster Controller:

Role: Monitors all processes (e.g., Repository, File Store) across nodes, detecting failures via heartbeats and status checks.

Failover Decision: If the active Repository stops responding, Cluster Controller initiates failover, promoting the passive instance to active.

Coordination: Works with Coordination Service (ZooKeeper) to update topology but makes the initial detection call.

Option A (Cluster Controller): Correct.

Why: It’s the watchdog process, constantly monitoring Repository health and triggering failover when needed.

Option B (Coordination Service): Incorrect.

Role: ZooKeeper maintains cluster state and coordinates topology updates post-failover, but doesn’t detect the failure—Cluster Controller does.

Option C (Gateway): Incorrect.

Role: Routes client requests—unrelated to internal process monitoring or failover.

Option D (Backgrounder): Incorrect.

Role: Executes background tasks—no involvement in Repository failover decisions.

Why This Matters: Understanding failover ensures HA reliability—Cluster Controller is the linchpin for resilience.

The VizQL Server process in Tableau Server handles rendering visualizations and processing queries for users viewing dashboards or workbooks. During installation, Tableau Server automatically configures the number of VizQL instances based on the number of processor cores on the machine, following this rule:

Default VizQL instances = 2 per node, unless manually adjusted post-installation.

In multi-node setups, additional instances may be added based on core count, but the question specifies a single server with 16 cores.

The installer does not scale VizQL instances linearly with core count by default (e.g., it doesn’t set 1 instance per 4 cores). Instead:

For a single-node installation, the default is 2 VizQL instances, regardless of core count (assuming the minimum hardware requirements are met: 8 cores, 32 GB RAM).

Administrators can later adjust this using TSM (e.g., tsm topology set-process) based on performance needs, but the question asks for the default configured by the installer.

Option A (4): Incorrect. Four instances might be configured manually for a 16-core server, but it’s not the default.

Option B (6): Incorrect. Six instances exceed the default for a single node.

Option C (10): Incorrect. Ten instances are far beyond the default and would require manual configuration.

Option D (2): Correct. The installer sets 2 VizQL instances by default on a single-node installation.

Data-driven alerts notify users when data meets conditions (e.g., sales > $10K). Let’s examine when Tableau checks these:

Alert Mechanics:

Enabled per site (Settings > General > Data-Driven Alerts).

Backgrounder evaluates conditions against extract data.

Check Intervals: Configurable via tsm configuration set -k dataAlerts.checkIntervalInMinutes (default: 60 minutes).

Refresh Trigger: Also checks post-extract refresh for immediate updates.

Option A (Every 60 minutes): Correct.

Details: Default interval—Backgrounder polls every hour to evaluate alert conditions.

Config: Adjustable (e.g., -v 30 for 30 minutes).

Option D (Whenever extracts in the related workbook are refreshed): Correct.

Details: An extract refresh updates the data, triggering an immediate alert check to catch changes promptly.

Why: Ensures timely notifications without waiting for the next poll.

Option B (Every 10 minutes): Incorrect.

Why: Not the default—possible via config, but not standard.

Option C (Whenever a new extract is published): Incorrect.

Why: Publishing creates the extract; alerts trigger on refreshes or polling, not initial publication.

Why This Matters: Timing affects alert responsiveness—balancing frequency and server load is key.

Tableau Server supports multiple authentication methods, including Local Authentication, Active Directory, Kerberos, SAML, and OpenID Connect. OpenID Connect (OIDC) is an identity layer built on OAuth 2.0, commonly used for single sign-on (SSO). In Tableau Server, OIDC is implemented as a variant of SAML (Security Assertion Markup Language) authentication because both are SSO protocols managed through the same configuration workflow.

To use OpenID Connect:

Configure Tableau Server for SAML/SSO.

Provide an OIDC-compatible identity provider (IdP) configuration (e.g., Google, Okta).

Set up the IdP metadata and certificates in TSM.

Option D (SAML): Correct. Tableau Server treats OIDC as a subset of its SAML authentication framework, so you configure it under the SAML settings in TSM.

Option A (Local Authentication): Incorrect. Local Authentication uses Tableau’s internal user database, not an external SSO protocol like OIDC.

Option B (Kerberos): Incorrect. Kerberos is a network authentication protocol for Windows environments, unrelated to OIDC.

Option C (Active Directory): Incorrect. AD uses LDAP or Kerberos, not OIDC, for authentication.

When a user reports slow performance for a newly-published workbook on Tableau Server, troubleshooting requires isolating the cause—e.g., data source issues, server load, workbook design, or caching. The first question should establish a baseline to narrow the scope. Let’s analyze this step-by-step with depth:

Performance Context:

A workbook’s speed depends on:

Data Source: Query complexity, size, network latency (e.g., database vs. extract).

Workbook Design: Filters, calculations, dashboard complexity.

Server Resources: VizQL rendering, Backgrounder load, caching.

"Newly-published" implies it’s not yet optimized or cached on the server.

Option A (Does it run any faster in Tableau Desktop?): Correct.

Why First: Comparing Desktop vs. Server performance is the most foundational diagnostic step:

Desktop Baseline: If it’s slow in Desktop (local machine), the issue likely lies in the workbook (e.g., complex queries, large data) or data source (e.g., slow database)—not Server-specific.

Server Difference: If it’s fast in Desktop but slow on Server, the problem could be server-side (e.g., resource contention, network latency to the data source from Server).

Practical Next Steps:

Slow in Desktop: Optimize workbook (e.g., simplify calcs, use extracts).

Fast in Desktop: Check Server (e.g., caching, VizQL load).

Why Critical: Establishes whether the issue is inherent to the workbook/data or introduced by Server—guides all further investigation.

Option B (Does the workbook always run slowly or does performance vary?): Useful but secondary.

Why Not First: Variability (e.g., slow at peak times) points to server load, but without a Desktop baseline, you can’t rule out workbook design. It’s a follow-up question after A.

Detail: Variability might suggest caching or concurrent user impact, but it assumes Server-side causation prematurely.

Option C (How many times have you opened the workbook in Tableau Server?): Less relevant initially.

Why Not First: Frequency of access might affect caching (first load is slower, subsequent loads faster), but it’s too specific and doesn’t isolate Desktop vs. Server. It’s a niche follow-up.

Option D (Did you enable caching on the workbook?): Misleading and incorrect.

Why Not First: Caching is server-managed (e.g., VizQL cache settings via tsm data-access caching set), not a user-toggle per workbook. Users don’t "enable" it—admins do. Plus, it’s premature without a baseline.

Why This Matters: Starting with Desktop performance cuts through assumptions, pinpointing whether the root cause is client-side (workbook/data) or server-side—essential for efficient resolution in production.

Kerberos delegation allows Tableau Server to pass a user’s Kerberos credentials to a data source for seamless authentication (SSO)—let’s explore which sources support it:

Kerberos Overview:

Used with Active Directory (AD) for SSO in Windows environments.

Tableau Server delegates the user’s ticket to the data source, avoiding embedded credentials.

Requires:

Data source support for Kerberos.

Proper configuration (e.g., SPN, constrained delegation).

Supported Data Sources: Per Tableau’s documentation:

Option A (Teradata): Correct.

Details: Supports Kerberos delegation—common in enterprise data warehouses.

Config: Enable in TSM (tsm authentication kerberos configure) and set SPN for Teradata.

Option C (SQL Server): Correct.

Details: Fully supports Kerberos—widely used with AD-integrated SQL Server instances.

Config: Requires AD setup and "Trustworthy" delegation in SQL Server.

Option D (SAP HANA): Correct.

Details: Supports Kerberos SSO via delegation—popular in SAP ecosystems.

Config: Needs HANA Kerberos setup (e.g., keytab) and Tableau Server integration.

Option B (PostgreSQL): Incorrect.

Why: Supports Kerberos authentication natively, but Tableau Server doesn’t enable delegation to PostgreSQL—users must embed credentials or use other methods (e.g., OAuth).

Why This Matters: Kerberos delegation enhances security by avoiding stored passwords—knowing supported sources ensures SSO feasibility.

The Backgrounder process in Tableau Server handles tasks like extract refreshes and subscriptions—let’s explore its concurrency:

Backgrounder Behavior:

Each instance is single-threaded for task execution—one task at a time per Backgrounder.

Multiple Backgrounders (e.g., in multi-node setups) increase parallelism, but a single Backgrounder is limited to 1 concurrent task.

Queue: Additional tasks wait in the queue, prioritized by their priority (1–100).

Option A (One): Correct.

Details: A single Backgrounder executes one task (e.g., an extract refresh) until completion before starting the next.

Config: Add more Backgrounders via TSM (tsm topology set-process -n node1 -pr backgrounder -c 2) for more concurrency.

Option B (Two): Incorrect.

Why: Not natively supported—a single Backgrounder doesn’t multi-thread tasks.

Option C (Three): Incorrect.

Why: Exceeds the single-threaded design.

Option D (Unlimited): Incorrect.

Why: Concurrency is fixed at 1 per instance—resources affect queue processing speed, not simultaneous tasks.

Why This Matters: Understanding Backgrounder limits guides scaling—more instances mean more parallel tasks, critical for heavy workloads.

Backing up Tableau Server ensures recovery from failures or migrations. A full backup includes multiple data types—let’s dissect this comprehensively:

Backup Components:

Repository Data: PostgreSQL database with metadata (users, permissions, workbooks). Backed up via tsm maintenance backup -f .tsbak.

Configuration Data: Server settings (e.g., ports, authentication) also in the .tsbak file.

Server Secrets: Encryption keys, internal tokens, Repository passwords—critical for restoring functionality.

Extracts: .hyper files in File Store (optional, separate backup).

Option A (Server secrets and Repository passwords): Correct.

Details: Includes encryption keys (for extracts), internal tokens (process communication), and Repository credentials. Backed up separately or stored securely (e.g., tsm security export-keys).

Why Critical: Without these, restored data may be inaccessible or services may fail.

Option C (Configuration data): Correct.

Details: Ports, authentication settings, process topology—part of the .tsbak file.

Why Critical: Restores server behavior and connectivity post-recovery.

Option D (Repository data): Correct.

Details: Core metadata database—also in .tsbak.

Why Critical: Without it, all content and user data is lost.

Option B (Topology data): Incorrect.

Details: Topology (process distribution) is part of configuration data in the .tsbak, not a separate entity. It’s not distinctly backed up as “topology data.”

Why This Matters: A complete backup (secrets, config, repository) ensures full restoration—missing any piece risks an unusable server.