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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Architecture | 30% | - Data modeling and use cases - Data sharing architecture design - Object hierarchy and architectural impact - Data protection and recovery strategies - Development lifecycle and workload support |
| Performance Optimization | 20% | - Cost optimization and resource management - Virtual warehouse configuration and sizing - Clustering and partitioning strategies - Query performance tuning |
| Accounts and Security | 25% | - Security principles and implementation scenarios - Security, compliance and governance architecture - Account and database strategy design |
| Data Engineering | 25% | - Data loading and unloading solutions - Transformation and processing frameworks - Data pipeline and integration design |
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NEW QUESTION # 75
Which of the below commands will use warehouse credits?
Answer: A,B,D
Explanation:
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES
NEW QUESTION # 76
A Snowflake account has the following parameters:
* MIN_DATA_RETENTION_TIME_IN_DAYS is set to 5 at the account level.
* DATA_RETENTION_TIME_IN_DAYS is set to 4 on database DB1, and 6 on Schema1 in DB1.
* DATA_RETENTION_TIME_IN_DAYS is set to 5 on database DB2, and 8 on Schema2 in DB2.What will be the result?
Answer: A
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:
Snowflake Time Travel retention is governed by a parameter hierarchy and by edition-dependent limits, but the key behavior in this scenario is the minimum retention guardrail.
MIN_DATA_RETENTION_TIME_IN_DAYS sets a floor: objects cannot effectively use a DATA_RETENTION_TIME_IN_DAYS value below that minimum. With an account-level minimum of 5 days, a database-level setting of 4 days is below the permitted minimum, so the effective retention for DB1 cannot be 4 days; it must be at least 5 days. Meanwhile, Schema1 is set to 6 days, which is above the minimum and should apply at the schema level (schemas can override their parent database settings within allowed bounds). For DB2, the database setting is 5 days, which matches the minimum and is valid; Schema2 at 8 days is also valid and applies to objects under that schema where applicable. From an architectural and operational perspective, this highlights two exam-relevant points: (1) retention is controlled via hierarchical parameters (account # database # schema # table), and (2) minimum retention settings enforce governance constraints across environments, preventing overly aggressive reductions that might violate recovery requirements.
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NEW QUESTION # 77
What are characteristics of the use of transactions in Snowflake? (Select TWO).
Answer: B,D
NEW QUESTION # 78
What is a characteristic of event notifications in Snowpipe?
Answer: D
Explanation:
Event notifications in Snowpipe are messages sent by cloud storage providers to notify Snowflake of new or modified files in a stage. Snowpipe uses these notifications to trigger data loading from the stage to the target table. When a pipe is paused, event messages received for the pipe enter a limited retention period, which varies depending on the cloud storage provider. If the pipe is not resumed within the retention period, the event messages will be discarded and the data will not be loaded automatically. To load the data, the pipe must be resumed and the COPY command must be executed manually. This is a characteristic of event notifications in Snowpipe that distinguishes them from other options. References: Snowflake Documentation: Using Snowpipe, Snowflake Documentation: Pausing and Resuming a Pipe
NEW QUESTION # 79
An event table has 150B rows and 1.5M micro-partitions, with the following statistics:
Column NDV*
A_ID 11K
C_DATE 110
NAME 300K
EVENT_ACT_0 1.1G
EVENT_ACT_4 2.2G
*NDV = Number of Distinct Values
What three clustering keys should be used, in order?
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:
Clustering keys are most beneficial when they improve micro-partition pruning for common filter patterns and when the chosen columns provide a useful ordering that co-locates data. A common heuristic is to place lower- cardinality columns earlier (to quickly narrow partitions) and then add a higher-cardinality column that further reduces scanned partitions for selective access paths. Here, C_DATE has very low NDV (110), making it an excellent leading key to organize data by date and enable strong pruning for time-bound queries typical of event tables. Next, A_ID (11K) is moderate cardinality and can further segment data within a date range, helping point lookups or narrow scans by identifier. For the third key, the options force choosing between very high-cardinality event activity columns; selecting EVENT_ACT_0 (1.1G) is preferable to EVENT_ACT_4 (2.2G) because it is comparatively less distinct while still supporting additional pruning when queries filter by that attribute. This ordering aligns with Snowflake guidance: keep keys few, ordered to match common predicates, and avoid excessively high-cardinality keys unless they directly match frequent selective filters.
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NEW QUESTION # 80
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