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Snowflake SnowPro Advanced Administrator Sample Questions (Q26-Q31):
NEW QUESTION # 26
A company has implemented Snowflake replication between two Snowflake accounts, both of which are running on a Snowflake Enterprise edition. The replication is for the database APP_DB containing only one schema, APP_SCHEMA. The company's Time Travel retention policy is currently set for 30 days for both accounts. An Administrator has been asked to extend the Time Travel retention policy to 60 days on the secondary database only.
How can this requirement be met?
- A. Set the data retention policy on the primary database to 30 days and the schemas to 60 days.
- B. Set the data retention policy on the secondary database to 60 days.
- C. Set the data retention policy on the primary database to 60 days.
- D. Set the data retention policy on the schemas in the secondary database to 60 days.
Answer: B
Explanation:
Explanation
According to the Replication considerations documentation, the Time Travel retention period for a secondary database can be different from the primary database. The retention period can be set at the database, schema, or table level using the DATA_RETENTION_TIME_IN_DAYS parameter. Therefore, to extend the Time Travel retention policy to 60 days on the secondary database only, the best option is to set the data retention policy on the secondary database to 60 days using the ALTER DATABASE command. The other options are incorrect because:
*B. Setting the data retention policy on the schemas in the secondary database to 60 days will not affect the database-level retention period, which will remain at 30 days. The most specific setting overrides the more general ones, so the schema-level setting will apply to the tables in the schema, but not to the database itself.
*C. Setting the data retention policy on the primary database to 30 days and the schemas to 60 days will not affect the secondary database, which will have its own retention period. The replication process does not copy the retention period settings from the primary to the secondary database, so they can be configured independently.
*D. Setting the data retention policy on the primary database to 60 days will not affect the secondary database, which will have its own retention period. The replication process does not copy the retention period settings from the primary to the secondary database, so they can be configured independently.
NEW QUESTION # 27
A retailer uses a TRANSACTIONS table (100M rows, 1.2 TB) that has been clustered by the STORE_ID column (varchar(50)). The vast majority of analyses on this table are grouped by STORE_ID to look at store performance.
There are 1000 stores operated by the retailer but most sales come from only 20 stores. The Administrator notes that most queries are currently experiencing poor pruning, with large amounts of bytes processed by even simple queries.
Why is this occurring?
- A. The STORE_ID should be numeric.
- B. Sales across stores are not uniformly distributed.
- C. The cardinality of the stores to transaction count ratio is too low to use the STORE_ID as a clustering key.
- D. The table is not big enough to take advantage of the clustering key.
Answer: B
Explanation:
According to the Snowflake documentation1, clustering keys are most effective when the data is evenly distributed across the key values. If the data is skewed, such as in this case where most sales come from only 20 stores out of 1000, then the micro-partitions will not be well-clustered and the pruning will be poor. This means that more bytes will be scanned by queries, even if they filter by STORE_ID. Option A is incorrect because the data type of the clustering key does not affect the pruning. Option B is incorrect because the table is large enough to benefit from clustering, if the data was more balanced. Option D is incorrect because the cardinality of the clustering key is not relevant for pruning, as long as the key values are distinct.
1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
NEW QUESTION # 28
A Snowflake Administrator wants to create a virtual warehouse that supports several dashboards, issuing various queries on the same database.
For this warehouse, why should the Administrator consider setting AUTO_SUSPEND to 0 or NULL?
- A. To keep the query result cache warm for good performance on repeated queries
- B. To save costs by running the warehouse as little as possible
- C. To keep the data cache warm to support good performance of similar queries
- D. To save costs on warehouse shutdowns and startups for different queries
Answer: C
Explanation:
According to the Snowflake documentation1, the AUTO_SUSPEND property specifies the number of seconds of inactivity after which a warehouse is automatically suspended. If the property is set to 0 or NULL, the warehouse never suspends automatically. For a warehouse that supports several dashboards, issuing various queries on the same database, setting AUTO_SUSPEND to 0 or NULL can help to keep the data cache warm, which means that the data used by the queries is already loaded into the warehouse memory and does not need to be fetched from the storage layer. This can improve the performance of similar queries that access the same data. Option A is incorrect because setting AUTO_SUSPEND to 0 or NULL does not save costs on warehouse shutdowns and startups, but rather increases the costs by keeping the warehouse running continuously. Option B is incorrect because setting AUTO_SUSPEND to 0 or NULL does not run the warehouse as little as possible, but rather runs the warehouse as much as possible. Option D is incorrect because setting AUTO_SUSPEND to 0 or NULL does not affect the query result cache, which is a separate cache that stores the results of previous queries for a period of time. The query result cache is not dependent on the warehouse state, but on the query criteria2.
NEW QUESTION # 29
DatabaseA has a single schema called Schema1. This schema contains many tables and views. The ANALYST role has privileges to select from all objects in DatabaseA. Schema1. The SYSADMIN role clones DatabaseA to DatabaseA_clone.
What privileges does the ANALYST role have on tables and views in DatabaseA_clone? (Select TWO).
- A. SELECT on all tables, and only secure views in DatabaseA_clone. Schemal
- B. SELECT on all tables, and only non-secure views in DatabaseA_clone. Schemal
- C. USAGE on the schema DatabaseA clone
- D. USAGE on the database DatabaseA_clone. Schemal
- E. SELECT on all tables and views in DatabaseA_clone. Schema1
Answer: B,E
Explanation:
Explanation
According to the Snowflake documentation, when a database or schema is cloned, the clone inherits all granted privileges on the clones of all child objects contained in the source object, such as tables and views.
However, the clone of the container itself does not inherit the privileges granted on the source container.
Therefore, the ANALYST role will have SELECT privilege on all tables and views in DatabaseA_clone.Schema1, but not USAGE privilege on the database or schema. The type of view (secure or non-secure) does not affect the cloning of privileges.
NEW QUESTION # 30
A Snowflake user runs a complex SQL query on a dedicated virtual warehouse that reads a large amount of data from micro-partitions. The same user wants to run another query that uses the same data set.
Which action would provide optimal performance for the second SQL query?
- A. Use the RESULT_SCAN function to post-process the output of the first query.
- B. Assign additional clusters to the virtual warehouse.
- C. Increase the STATEMENT_TIMEOUT_IN_SECONDS parameter in the session.
- D. Prevent the virtual warehouse from suspending between the running of the first and secondqueries.
Answer: A
Explanation:
Explanation
According to the Using Persisted Query Results documentation, the RESULT_SCAN function allows you to query the result set of a previous command as if it were a table. This can improve the performance of the second query by avoiding reading the same data from micro-partitions again. The other actions do not provide optimal performance for the second query because:
*Assigning additional clusters to the virtual warehouse does not affect the data access speed, but only the query execution speed. It also increases the cost of the warehouse.
*Increasing the STATEMENT_TIMEOUT_IN_SECONDS parameter in the session does not improve the performance of the query, but only allows it to run longer before timing out. It also increases the risk of resource contention and deadlock.
*Preventing the virtual warehouse from suspending between the running of the first and second queries does not guarantee that the data will be cached in memory, as Snowflake uses a least recently used (LRU) cache eviction policy. It also increases the cost of the warehouse.
https://docs.snowflake.com/en/user-guide/querying-persisted-results
NEW QUESTION # 31
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