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In a scenario where Tableau Server’s dashboards are frequently updated with real-time data, what
caching strategy should be employed to optimize performance?
A. Configuring the server to use a very long cache duration to maximize the use of cached data B. Setting the cache to refresh only during off-peak hours to reduce the load during high-usage periods C. Adjusting the cache to balance between frequent refreshes and maintaining some level of cached
data D. Utilizing disk-based caching exclusively to handle the high frequency of data updates
Answer: C
Explanation:
Adjusting the cache to balance between frequent refreshes and maintaining some level of cached data
For dashboards that are frequently updated with real-time data, the caching strategy should aim to
balance between frequent cache refreshes and maintaining a level of cached data. This approach allows
for relatively up-to-date information to be displayed while still taking advantage of caching for improved
performance. Option A is incorrect because a very long cache duration may lead to stale data being
displayed in scenarios with frequent updates. Option B is incorrect as refreshing the cache only during
off-peak hours might not be suitable for dashboards requiring real-time data. Option D is incorrect
because relying solely on disk-based caching does not address the need for balancing cache freshness
with performance in a real-time data scenario.
Question # 2
If load testing results for Tableau Server show consistently low utilization of CPU and memory re-sourceseven under peak load, what should be the next step?
A. Further increase the load in subsequent tests to find the server's actual performance limits B. Immediately scale down the server's hardware to reduce operational costs C. Focus on testing network bandwidth and latency as the primary factors for performance optimization D. Stop further load testing as low resource utilization indicates optimal server performance
Answer: A Explanation:
Further increase the load in subsequent tests to find the server’s actual performance limits If load
testing shows low utilization of CPU and memory resources under peak load, the next step is to increase
the load in subsequent tests. This helps in determining the actual limits of the server’s performance and
ensures that the server is tested adequately against potential real-world high-load scenarios. Option B is
incorrect because scaling down hardware prematurely might not accommodate unexpected spikes in
usage or future growth. Option C is incorrect as focusing solely on network factors without fully
understanding the server’s capacity limits may overlook other performance improvement areas. Option
D is incorrect because stopping further testing based on initial low resource utilization may lead to an
incomplete understanding of the server’s true performance capabilities.
Question # 3
During the validation of a disaster recovery/high availability strategy for Tableau Server, what is a keyelement to test to ensure data integrity?
A. Frequency of complete system backups B. Speed of the failover to a secondary server C. Accuracy of data and dashboard recovery post-failover D. Network bandwidth availability during the failover process
Answer: C
Explanation:
Accuracy of data and dashboard recovery post-failover The accuracy of data and dashboard recovery
post-failover is crucial in validating a disaster recovery/high availability strategy. This ensures that after a
failover, all data, visualizations, and dashboards are correctly re-stored and fully functional, maintaining
the integrity and continuity of business operations. Option A is incorrect because while the frequency of
backups is important, it does not directly validate the effectiveness of data recovery in a disaster
scenario. Option B is incorrect as the speed of failover, although important for minimizing downtime,
does not alone ensure data integrity post-recovery. Option D is incorrect because network bandwidth,
while impacting the performance of the failover process, does not directly relate to the accuracy and
integrity of the recovered data and dashboards.
Question # 4
When installing and configuring the Resource Monitoring Tool (RMT) server for Tableau Server, whichaspect is crucial to ensure effective monitoring?
A. Configuring RMT to monitor all network traffic to and from the Tableau Server B. Ensuring RMT server has a dedicated database for storing monitoring data C. Setting up RMT to automatically restart Tableau Server services when performance thresholds are
exceeded D. Installing RMT agents on each node of the Tableau Server cluster
Answer: D
Explanation:
Installing RMT agents on each node of the Tableau Server cluster For the Re-source Monitoring Tool to effectively monitor a Tableau Server deployment, it is essential to install RMT agents on each node of
the Tableau Server cluster. This ensures comprehensive monitoring of system performance, resource
usage, and potential issues across all components of the cluster. Option A is incorrect because
monitoring all network traffic is not the primary function of RMT; it is focused more on system
performance and resource utilization. Option B is incorrect as having a dedicated database for RMT is
beneficial but not crucial for the basic monitoring functionality. Option C is incorrect because automatic
restart of services is not a standard or recommended feature of RMT and could lead to unintended
disruptions.
Question # 5
You identify that a particular Tableau data source is causing slow query performance. What should be
your initial approach to resolving this issue?
A. Restructuring the underlying database to improve its performance B. Optimizing the data source by reviewing and refining complex calculations and data relationships C. Replacing the data source with a pre-aggregated summary data source D. Increasing the frequency of extract refreshes to ensure more up-to-date data
Answer: B Explanation:
Optimizing the data source by reviewing and refining complex calculations and data relationships The
initial approach to resolving slow query performance due to a data source should be to optimize the
data source itself. This includes reviewing complex calculations, data relationships, and query structures
within the data source to identify and address inefficiencies. This optimization can significantly improve
query performance without needing more drastic measures. Option A is incorrect as restructuring the
underlying database is a more extensive and complex solution that should be considered only if data
source optimization does not suffice. Option C is incorrect because replacing the data source with a
preaggregated summary might not be feasible or appropriate for all analysis needs. Option D is incorrect
as increasing extract refresh frequency does not directly address the root cause of slow query
performance in the data source itself.
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