You manage an enterprise data warehouse in Azure Synapse Analytics. Users report slow performance when they run commonly used queries. Users do not report performance changes for infrequently used…

Question

Options

• ADWU limit
• BCache hit percentage
• CLocal tempdb percentage
• DData IO percentage

• A
  2% (1)
• B
  88% (38)
• C
  2% (1)
• D
  7% (3)

Explanation

Cache hit percentage is correct because Azure Synapse Analytics uses a multi-layer cache (in-memory columnstore and SSD-based result cache) to accelerate frequently run queries. A low cache hit percentage means commonly used queries aren't being served from cache, forcing expensive reads from storage - which directly explains why frequent queries are slow while infrequent ones (which wouldn't be cached regardless) show no change.

• A (DWU limit) reflects overall compute capacity, not whether cached data is being leveraged; it wouldn't isolate the pattern of frequent vs. infrequent query performance.
• C (Local tempdb percentage) relates to temporary storage usage during query execution (sorts, spills), not caching behavior for repeated queries.
• D (Data IO percentage) measures raw disk I/O load, which is a symptom of poor cache utilization rather than the root cause metric that explains the frequent-vs-infrequent pattern.

Memory tip: Think "frequently used = should be cached." If common queries are slow, the cache isn't doing its job - so monitor Cache Hit % to confirm. The key clue in the question is the contrast between common and infrequent queries, which is a dead giveaway for a caching problem.

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