Explanation:

Why This is the Correct Approach:

Tableau Server relies on multiple processes to handle different tasks, and balancing them is critical for performance:

VizQL: Renders visualizations for users (needs more instances if many users access dashboards simultaneously).

Data Server: Manages data extracts and live connections (important if the business uses many extracts).

Backgrounder: Runs scheduled refreshes and subscriptions (needs enough capacity to avoid delays).

A medium-sized business should adjust process counts based on:

Concurrent users (more VizQL if many users).

Extract usage (more Data Server if heavy on extracts).

Refresh schedules (more Backgrounder if many automated tasks).

Why the Other Options Are Not Ideal:

A) Excessive processes:
Wastes server resources and can slow down performance due to overhead.

B) Equal processes for all types:
Doesn’t account for actual usage patterns (e.g., may need more VizQL than Backgrounder).

D) Only prioritizing VizQL:
Neglects data refreshes and extract performance, leading to bottlenecks.

Best Practices for Medium-Sized Businesses:
Start with a balanced baseline (e.g., 4-6 VizQL, 3-4 Backgrounder, 2-3 Data Server).

Monitor performance using Tableau’s Admin Views to adjust as needed.

Scale up processes only where necessary (e.g., add VizQL if dashboard load times increase).

Key Takeaway:
Tailor process counts to match real-world usage—don’t over-allocate or assume all processes need equal resources.

Explanation:

Why B is Correct?

High-performance server cluster: Distributes the workload across multiple nodes (e.g., VizQL, Backgrounder, Data Server) to handle thousands of concurrent users without bottlenecks.

Load balancing: Ensures even traffic distribution, preventing any single node from being overwhelmed.

High-speed network: Critical for minimizing latency in global operations (e.g., 10 Gbps+ connections between nodes and data centers).

Tableau’s Enterprise Deployment Guide recommends this architecture for large-scale deployments.

Why Other Options Are Incorrect?

A. Minimal hardware: Will fail under heavy load, causing downtime and poor user experience.

C. Standard hardware/no advanced network: Inadequate for global scale—leads to slow performance and user frustration.

D. Single high-capacity server: A single point of failure; lacks scalability and redundancy.

Key Specifications for Global Enterprise Deployment:

Multi-node cluster (e.g., 3+ nodes for failover).

High CPU/RAM (e.g., 16+ cores, 64+ GB RAM per node)

. Network:

Low-latency, high-bandwidth connections (e.g., dedicated 10 Gbps links).

Content Delivery Network (CDN) for global users.

Storage:

SSD/NVMe for fast I/O (extracts, queries).

Reference:

Tableau’s Hardware Sizing Guide for large enterprises.

Final Note:

For global enterprises, B is the only viable option. Options A/C/D risk performance failures and unhappy users. Always design for scalability and redundancy.

Explanation:

Why B is Correct?

Reverse proxy misconfiguration (e.g., incorrect URL rewriting, mismatched ports, or missing headers) is the most common cause of intermittent access issues when Tableau Server is behind a proxy.

Key checks:

URL rewriting rules: Ensure paths like /tabsvc or /vizql are correctly proxied to Tableau Server.

Port forwarding: Verify the proxy forwards requests to Tableau’s ports (default: 80/443 or custom ports).

HTTP headers: Headers like X-Forwarded-For must be preserved for Tableau’s load balancing and logging.

Tableau’s Reverse Proxy Guide emphasizes these settings.

Why Other Options Are Incorrect?

A. Increasing bandwidth: Unrelated to proxy configuration—intermittent issues suggest routing/configuration errors, not throughput.

C. Changing proxy software: Premature—first diagnose the current setup (e.g., Nginx/Apache configs).

D. Installing a new SSL certificate: SSL issues cause persistent errors (not intermittent ones) and are irrelevant if the proxy handles SSL termination.

Troubleshooting Steps:

Review proxy logs for errors (e.g., 404 or 502 errors):

Nginx: /var/log/nginx/error.log

Validate URL rewriting:

Ensure requests to https://proxy.example.com/tableau route to http://tableau-server:80/.

Test direct access (bypass the proxy):

Temporarily access Tableau Server’s IP:port to confirm the issue is proxy-specific.

Reference:

Tableau’s Troubleshooting Reverse Proxy Issues highlights URL rewriting as the top culprit.

Final Note:

Always start with proxy configuration (B)—intermittent issues typically stem from misrouted requests or missing headers. If problems persist, check firewall rules or Tableau Server logs

Explanation:

Why A is Correct?

Active-active mode ensures high availability (HA) and minimal downtime by

Real-time replication: Both servers synchronize data (e.g., extracts, permissions) continuously.

Automatic failover: If the primary fails, the secondary takes over seamlessly.

Tableau’s Disaster Recovery Guide recommends this for mission-critical deployments.

Why Other Options Are Insufficient?

B. Nightly same-server backups: Risk losing a day’s data and don’t reduce downtime.

C. Manual weekly backups: Too slow—businesses need real-time recovery.

D. Cloud-only backups: Lacks failover capability—restores take hours.

Key Components of Automated DR:

Multi-node cluster:

Primary + standby nodes (configured via tsm topology).

Regular DR drills:

Test failover quarterly.

Reference:

NIST SP 800-34 on disaster recovery.

Final Note:

A is the only zero-downtime solution. Options B/C/D risk data loss or prolonged outages. Always validate failover in staging.

Explanation:

Why B is Correct?

Tableau Server includes built-in log management tools (e.g., tabadmin log, tsm maintenance ziplogs, Admin Views) designed for efficient log collection and analysis.

Targeted log review during peak issue times (e.g., slow dashboards, crashes) helps pinpoint root causes faster.

Automated tools like Tableau Server's "Log Shipper" can forward logs to centralized systems (e.g., Splunk, ELK) for deeper analysis.

Tableau’s Logging Documentation recommends this approach.

Why Other Options Are Incorrect?

A. Storing logs only for critical errors: Misses context needed to diagnose intermittent or performance-related issues.

C. Manual daily collection: Inefficient and risks missing real-time data—logs should be gathered proactively when issues arise.

D. Relying solely on third-party tools: Tableau’s native tools understand log structure best; third-party tools should complement, not replace them.

Best Practices for Log Analysis:

Use tsm maintenance ziplogs to collect all relevant logs in one bundle.

Filter logs by timestamp during reported issues (e.g., grep "ERROR" vizqlserver.log).

Monitor Admin Views (e.g., "Background Tasks" or "Performance") to correlate logs with server metrics.

Reference:

Tableau’s Troubleshooting Guide highlights log collection as step one.

Final Note:

B is the most effective strategy—leveraging built-in tools ensures accuracy and timeliness. Options A/C/D either limit data or add unnecessary complexity.