Explanation:

Why Option B is Correct:

Cache optimization in Tableau Server balances performance (reduced query load) with data freshness.

Clearing the cache on a schedule (e.g., after ETL jobs refresh source data) ensures:

Performance benefits of cached queries.

Data consistency by invalidating stale cache entries.

Reference: Tableau Server Performance Tuning Guide.

Why Other Options Are Incorrect:

A) Refresh cache on every view load:
Defeats the purpose of caching (no performance gain).

C) Disabling caching:
Forces real-time queries, increasing server load and latency.

D) Increasing RAM:
Helps caching but doesn’t address cache invalidation logic.

Key Cache Settings in Tableau Server:

vizqlserver.session.expiry.timeout: Controls how long cached sessions persist.

Backgrounder tasks: Schedule cache clears post-data-refresh.

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 C is Correct?

A scalable deployment model (e.g., multi-node cluster for Tableau Server or Tableau Cloud) is critical for a rapidly growing retail company because it:

Handles increasing users: More stores/employees = more concurrent dashboard viewers.

Supports larger datasets: Sales, inventory, and customer data grow over time.

Adapts to seasonal spikes: Black Friday, holidays, etc.

Tableau’s Scalability Guide recommends planning for 2–3x current needs.

Why Other Options Are Incorrect?

A. Limiting users: Stifles business growth—scalability means supporting more users, not fewer.

B. Ignoring future growth: Leads to costly re-architecting later (e.g., migrating from single-node to cluster).

D. Single server: A single point of failure—cannot handle nationwide scale.

Scalable Deployment Options:

Tableau Server (Multi-Node Cluster):

Distribute workloads (VizQL, Backgrounder) across nodes.

Use load balancers for high availability.

Tableau Cloud:

Automatically scales with usage (managed by Tableau).

Hybrid (Server + Cloud):

Use Cloud for peak demand, Server for sensitive data.

Reference:

Tableau’s Enterprise Deployment Guide.

Final Note:

C is the only future-proof choice. Options A/B/D risk outages or stagnation. Always benchmark performance and plan for growth.

Explanation:

Why B is Correct?

A valid SSL certificate from a trusted CA (e.g., DigiCert, Let’s Encrypt, or an enterprise PKI) is essential for:

Authenticating Tableau Server’s identity to clients (preventing man-in-the-middle attacks).

Enabling strong encryption (TLS 1.2/1.3) for data in transit.

Self-signed certificates trigger browser warnings and are not trusted by default, breaking secure communication.

Tableau’s SSL Configuration Guide mandates CA-signed certificates for production environments.

Why Other Options Are Incorrect?

A. Configuring algorithms: While important (e.g., disabling weak ciphers), it’s secondary to having a valid certificate.

C. SSL decryption server: Unnecessary—Tableau Server handles SSL natively. This would introduce complexity and potential vulnerabilities.

D. Enabling SSL on clients: Clients automatically use SSL when Tableau Server enforces HTTPS. No client-side SSL setup is required.

Critical Steps for SSL Implementation:

Obtain a CA-signed certificate (wildcard or SAN certificate for multi-node deployments).

Verify SSL/TLS settings (e.g., disable outdated protocols like TLS 1.0/1.1).

Reference:

NIST Guidelines (SP 800-52): Require CA-signed certificates for federal systems (aligns with HIPAA/PCI DSS).

Tableau’s Security Hardening Guide prioritizes CA certificates.

Final Note:

Without a trusted certificate (B), SSL encryption is incomplete and untrusted. Always start with this step before fine-tuning algorithms (A).

Explanation:

Why B is Correct?

Incremental load testing helps identify performance thresholds, bottlenecks, and scalability limits without overwhelming the server abruptly.

It mirrors real-world usage patterns (e.g., growing user traffic) and reveals:

Response time degradation (e.g., dashboards slowing at 500+ users).

Resource saturation points (e.g., CPU/memory spikes).

Tableau’s Performance Testing Guide recommends this approach.

Why Other Options Are Less Effective?

A. Testing only during peak hours: Limits understanding of progressive failure modes and risks disrupting live users.

C. Synthetic data only: Real-world data (with its complexities) is critical for accurate performance insights.

D. Focusing only on new features: Neglects end-to-end system performance (e.g., legacy features may bottleneck new ones).

Key Steps for Valid Load Testing:

Baseline testing: Measure performance under normal loads.

Ramp-up: Gradually add users/requests (e.g., +100 users every 5 minutes).

Monitor metrics: CPU, memory, query latency, and error rates.

Repeat with real data: Ensure tests reflect production scenarios

. Reference:

Load Testing Best Practices (NIST SP 800-146): Emphasizes incremental load increases.

Final Note:

B is the only method that balances realism and safety. Options A/C/D risk skewed results or system crashes. Always simulate realistic growth patterns