Snowflake SnowPro Core Practice Test 80
Question 790
For an original SnowPro Core educational scenario, consider: A team enables Query Acceleration Service and assumes every query, including tiny metadata queries, must become faster. Select TWO complementary steps: the targeted correction and its strongest prechange validation.
- Check: Inspect query eligibility and acceleration history plus incremental service credits for representative workloads.
- Check: Check query eligibility, refresh behavior, query rewrite usage and storage or maintenance charges.
- Declare that Query Acceleration Service substitutes for proper SQL design in every query.
- Change: Evaluate supported Query Acceleration Service on eligible queries using its estimate and cost controls.
- Change: Track query duration, warehouse and serverless credits, data volume and service objectives together for tuning decisions.
Explanation
Correct Answer
A:Query acceleration can offload eligible portions of selected workloads to serverless compute; not every query or operator benefits. Required intervention: Evaluate supported Query Acceleration Service on eligible queries using its estimate and cost controls.
D:Query acceleration can offload eligible portions of selected workloads to serverless compute; not every query or operator benefits. Evidence: Inspect query eligibility and acceleration history plus incremental service credits for representative workloads.
Incorrect Answers
B:Check: Check query eligibility, refresh behavior, query rewrite usage and storage or maintenance charges. Query acceleration can offload eligible portions of selected workloads to serverless compute; not every query or operator benefits.
C:For this select two assessment, the shortcut changes or bypasses the necessary correctness, governance or operational boundary rather than correcting it. Query acceleration can offload eligible portions of selected workloads to serverless compute; not every query or operator benefits.
E:Change: Track query duration, warehouse and serverless credits, data volume and service objectives together for tuning decisions. Query acceleration can offload eligible portions of selected workloads to serverless compute; not every query or operator benefits.
Question 791
A Snowflake engineer is examining this platform situation: A sales table joins to a customer history table with multiple rows per customer and aggregate revenue becomes inflated. A proposed shortcut is: Add DISTINCT to every result blindly without understanding the business grain or unmatched records. What should the Snowflake engineer recommend as the narrowly scoped correction? Select ONE best answer.
- Add DISTINCT to every result blindly without understanding the business grain or unmatched records.
- Evaluate supported Query Acceleration Service on eligible queries using its estimate and cost controls.
- Use QUERY_HISTORY and Query Profile metrics to compare consistent workloads, warehouse changes and time periods.
- Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
Explanation
Correct Answer
D:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. This targeted change addresses the stated technical or governance gap without discarding necessary controls.
Incorrect Answers
A:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. For this best change assessment, the shortcut changes or bypasses the necessary correctness, governance or operational boundary rather than correcting it.
B:Evaluate supported Query Acceleration Service on eligible queries using its estimate and cost controls. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:Use QUERY_HISTORY and Query Profile metrics to compare consistent workloads, warehouse changes and time periods. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 792
A Snowflake implementation review finds: A sales table joins to a customer history table with multiple rows per customer and aggregate revenue becomes inflated. The team proposes: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation. Which platform principle most strongly supports the recommended response? Select ONE best answer.
- Clustering can improve locality and pruning but consumes maintenance resources and is not a universal tuning step.
- SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
- Materialized views retain precomputed query results and have feature restrictions and maintenance tradeoffs.
- Grouping defines output grain and aggregate semantics, so duplicates and non-grouped columns can invalidate or distort totals.
Explanation
Correct Answer
B:Demonstrable success requires: The join yields expected grain and aggregate totals reconcile to source transactions. This principle explains the proposed technical approach, which is: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
Incorrect Answers
A:Clustering can improve locality and pruning but consumes maintenance resources and is not a universal tuning step. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:Materialized views retain precomputed query results and have feature restrictions and maintenance tradeoffs. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
D:Grouping defines output grain and aggregate semantics, so duplicates and non-grouped columns can invalidate or distort totals. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 793
The data platform team has not yet approved changes. Current evidence indicates: A sales table joins to a customer history table with multiple rows per customer and aggregate revenue becomes inflated. Which evidence best tests the technical diagnosis before approving a disruptive change? Select ONE best answer.
- Compare an equivalent subquery with QUALIFY and test tie handling using ordered timestamps.
- Check query eligibility, refresh behavior, query rewrite usage and storage or maintenance charges.
- Compare query profile, clustering depth, maintenance credits and representative workload results.
- Compare source key uniqueness, pre-join counts and resulting row counts for representative business IDs.
Explanation
Correct Answer
D:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. If confirmed, the proportionate intervention is: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
Incorrect Answers
A:Compare an equivalent subquery with QUALIFY and test tie handling using ordered timestamps. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
B:Check query eligibility, refresh behavior, query rewrite usage and storage or maintenance charges. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:Compare query profile, clustering depth, maintenance credits and representative workload results. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 794
A candidate correction has been tested and the platform team requires a post-change acceptance check. Original problem: A sales table joins to a customer history table with multiple rows per customer and aggregate revenue becomes inflated. The service owner needs positive and negative results and evidence that legitimate data workflows still function. Which acceptance result demonstrates correct platform behavior without disrupting authorized workflows? Select ONE best answer.
- Important queries improve enough to justify ongoing clustering expense without changing business results.
- Targeted point-lookups accelerate while maintenance costs remain proportional to business value.
- The pipeline refreshes reliably within an accepted practical interval and alerts on failures.
- The join yields expected grain and aggregate totals reconcile to source transactions.
Explanation
Correct Answer
D:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. The approved implementation step was: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
Incorrect Answers
A:Important queries improve enough to justify ongoing clustering expense without changing business results. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
B:Targeted point-lookups accelerate while maintenance costs remain proportional to business value. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:The pipeline refreshes reliably within an accepted practical interval and alerts on failures. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 795
An implementation sign-off request identifies the following problem: A sales table joins to a customer history table with multiple rows per customer and aggregate revenue becomes inflated. Which paired technical change and validation should close the implementation review? Select ONE best answer.
- Change: Evaluate concurrency scaling or workload separation and review queued overload time before rewriting correct SQL. Verify: Compare average execution versus queue time and cluster saturation during a simulated peak.
- Change: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation. Verify: Compare source key uniqueness, pre-join counts and resulting row counts for representative business IDs.
- Change: Evaluate persisted query result reuse conditions, including unchanged underlying data and eligible query determinism. Verify: Compare QUERY_HISTORY result reuse and query text or underlying data changes in a controlled test.
- Change: Evaluate supported Query Acceleration Service on eligible queries using its estimate and cost controls. Verify: Inspect query eligibility and acceleration history plus incremental service credits for representative workloads.
Explanation
Correct Answer
B:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. The change is successful only when: The join yields expected grain and aggregate totals reconcile to source transactions.
Incorrect Answers
A:Change: Evaluate concurrency scaling or workload separation and review queued overload time before rewriting correct SQL. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:Change: Evaluate persisted query result reuse conditions, including unchanged underlying data and eligible query determinism. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
D:Change: Evaluate supported Query Acceleration Service on eligible queries using its estimate and cost controls. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 796
A Snowflake engineer is examining this platform situation: A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs. A proposed shortcut is: Add DISTINCT to every result blindly without understanding the business grain or unmatched records. What should the Snowflake engineer recommend as the narrowly scoped correction? Select ONE best answer.
- Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
- Evaluate persisted query result reuse conditions, including unchanged underlying data and eligible query determinism.
- Add DISTINCT to every result blindly without understanding the business grain or unmatched records.
- Use QUALIFY to filter on the result of window functions after proper PARTITION BY and ORDER BY.
Explanation
Correct Answer
A:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. Applied case: A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs.
Incorrect Answers
B:Evaluate persisted query result reuse conditions, including unchanged underlying data and eligible query determinism. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. Use this instead: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
D:Use QUALIFY to filter on the result of window functions after proper PARTITION BY and ORDER BY. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 797
A Snowflake implementation review finds: A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs. The team proposes: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation. Which platform principle most strongly supports the recommended response? Select ONE best answer.
- Local warehouse cache is associated with running compute and does not equal reusable persisted query results across warehouses.
- MERGE performs conditional updates, deletes or inserts from source rows; nondeterministic source matches can cause errors or incorrect outcomes.
- SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
- Materialized views retain precomputed query results and have feature restrictions and maintenance tradeoffs.
Explanation
Correct Answer
C:Demonstrable success requires: The join yields expected grain and aggregate totals reconcile to source transactions. Applied case: A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs.
Incorrect Answers
A:This option addresses a different Snowflake technical topic (Warehouse local data cache) rather than the documented Join cardinality and key hygiene decision.
B:MERGE performs conditional updates, deletes or inserts from source rows; nondeterministic source matches can cause errors or incorrect outcomes. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
D:Materialized views retain precomputed query results and have feature restrictions and maintenance tradeoffs. A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs.
Question 798
The data platform team has not yet approved changes. Current evidence indicates: A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs. Which evidence best tests the technical diagnosis before approving a disruptive change? Select ONE best answer.
- Inspect profile operator times, spilled bytes, join row counts and warehouse configuration.
- Compare source key uniqueness, pre-join counts and resulting row counts for representative business IDs.
- Inspect query eligibility and acceleration history plus incremental service credits for representative workloads.
- Test row_number or moving-average output for customers with multiple timestamps and ties.
Explanation
Correct Answer
B:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. This check tests the actual data, compute or access boundary before an operationally risky change.
Incorrect Answers
A:Inspect profile operator times, spilled bytes, join row counts and warehouse configuration. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:Inspect query eligibility and acceleration history plus incremental service credits for representative workloads. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
D:Test row_number or moving-average output for customers with multiple timestamps and ties. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 799
A candidate correction has been tested and the platform team requires a post-change acceptance check. Original problem: A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs. The service owner needs positive and negative results and evidence that legitimate data workflows still function. Which acceptance result demonstrates correct platform behavior without disrupting authorized workflows? Select ONE best answer.
- Recurring reads improve without unacceptable data freshness or maintenance burden.
- The target reflects one authorized current record per business key after updates and inserts.
- The bottleneck is reduced with comparable results and defensible credit use.
- The join yields expected grain and aggregate totals reconcile to source transactions.
Explanation
Correct Answer
D:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. The result must be validated using the documented dataset or access context.
Incorrect Answers
A:Recurring reads improve without unacceptable data freshness or maintenance burden. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
B:The target reflects one authorized current record per business key after updates and inserts. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
C:The bottleneck is reduced with comparable results and defensible credit use. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
Question 800
For an original SnowPro Core educational scenario, consider: A developer uses a CROSS JOIN unintentionally and receives billions of output rows from two moderate inputs. Select THREE compatible elements: a proper correction, a targeted diagnostic and an acceptance result.
- Change: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
- Change: Use CREATE TABLE AS SELECT with an appropriate table type and explicit lifecycle or dependency management.
- Result: The join yields expected grain and aggregate totals reconcile to source transactions.
- Check: Inspect query profile scanned partitions, predicate form and representative date distribution.
- Result: Aggregated measures reconcile to the intended grain and avoid fanout inflation.
- Check: Compare source key uniqueness, pre-join counts and resulting row counts for representative business IDs.
Explanation
Correct Answer
A:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. Evidence: Compare source key uniqueness, pre-join counts and resulting row counts for representative business IDs.
C:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. Required intervention: Define correct join keys and grain, deduplicate where required and validate output cardinality before aggregation.
F:SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures. Acceptance: The join yields expected grain and aggregate totals reconcile to source transactions.
Incorrect Answers
B:Change: Use CREATE TABLE AS SELECT with an appropriate table type and explicit lifecycle or dependency management. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
D:Check: Inspect query profile scanned partitions, predicate form and representative date distribution. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
E:Result: Aggregated measures reconcile to the intended grain and avoid fanout inflation. SQL joins combine matching records according to relational cardinality; incorrect relationships multiply rows and distort measures.
