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Snowflake SnowPro Advanced Administrator Sample Questions (Q72-Q77):

NEW QUESTION # 72
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 save costs by running the warehouse as little as possible
B. To save costs on warehouse shutdowns and startups for different queries
C. To keep the data cache warm to support good performance of similar queries
D. To keep the query result cache warm for good performance on repeated 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 # 73
A Snowflake Administrator has a multi-cluster virtual warehouse and is using the Snowflake Business Critical edition. The minimum number of clusters is set to 2 and the maximum number of clusters is set to 10. This configuration works well for the standard workload, rarely exceeding 5 running clusters. However, once a month the Administrator notes that there are a few complex long-running queries that are causing increased queue time and the warehouse reaches its maximum limit at 10 clusters.
Which solutions will address the issues happening once a month? (Select TWO).

A. Increase the minimum number of clusters started in the multi-cluster configuration to 5.
B. Increase the multi-cluster maximum to 20 or more clusters.
C. Have the group running the complex monthly queries use a separate appropriately-sized warehouse to support their workload.
D. Use a task to increase the cluster size for the time period that the more complex queries are running and another task to reduce the size of the cluster once the complex queries complete.
E. Examine the complex queries and determine if they can be made more efficient using clustering keys or materialized views.

Answer: C,D

Explanation:
According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option A is a possible solution to address the issues happening once a month, as it allows the administrator to use a task to increase the cluster size for the time period that the more complex queries are running and another task to reduce the size of the cluster once the complex queries complete. This way, the warehouse can have more resources available to handle the complex queries without reaching the maximum limit of 10 clusters, and then return to the normal cluster size to save costs. Option B is another possible solution to address the issues happening once a month, as it allows the administrator to have the group running the complex monthly queries use a separate appropriately-sized warehouse to support their workload. This way, the warehouse can isolate the complex queries from the standard workload and avoid queue time and resource contention. Option C is not a recommended solution to address the issues happening once a month, as it would increase the costs and complexity of managing the multi-cluster warehouse, and may not solve the underlying problem of inefficient queries. Option D is a good practice to improve the performance of the queries, but it is not a direct solution to address the issues happening once a month, as it requires analyzing and optimizing the complex queries using clustering keys or materialized views, which may not be feasible or effective in all cases. Option E is not a recommended solution to address the issues happening once a month, as it would increase the costs and waste resources by starting more clusters than needed for the standard workload.

NEW QUESTION # 74
What are characteristics of Dynamic Data Masking? (Select TWO).

A. A single masking policy can be applied to columns with different data types.
B. A single masking policy can be applied to columns in different tables.
C. The role that creates the masking policy will always see unmasked data in query results.
D. A masking policy can be applied to the VALUE column of an external table.
E. A masking policy that is currently set on a table can be dropped.

Answer: A,B

Explanation:
Explanation
According to the Using Dynamic Data Masking documentation, Dynamic Data Masking is a feature that allows you to alter sections of data in table and view columns at query time using a predefined masking strategy. The following are some of the characteristics of Dynamic Data Masking:
*A single masking policy can be applied to columns in different tables. This means that you can write a policy once and have it apply to thousands of columns across databases and schemas.
*A single masking policy can be applied to columns with different data types. This means that you can use the same masking strategy for columns that store different kinds of data, such as strings, numbers, dates, etc.
*A masking policy that is currently set on a table can be dropped. This means that you can remove the masking policy from the table and restore the original data visibility.
*A masking policy can be applied to the VALUE column of an external table. This means that you can mask data that is stored in an external stage and queried through an external table.
*The role that creates the masking policy will always see unmasked data in query results. This is not true, as the masking policy can also apply to the creator role depending on the execution context conditions defined in the policy. For example, if the policy specifies that only users with a certain custom entitlement can see the unmasked data, then the creator role will also need to have that entitlement to see the unmasked data.

NEW QUESTION # 75
If the query matches the definition, will Snowflake always dynamically rewrite the query to use a materialized view?

A. Yes, because materialized views are always faster.
B. No, because joins are not supported by materialized views.
C. No, because the optimizer might decide against it.
D. No, because the materialized view may not be up-to-date.

Answer: C

Explanation:
Snowflake's query optimizer can automatically rewrite queries against the base table or regular views to use the materialized view instead, if the query matches the definition of the materialized view1. However, this is not always guaranteed, as the optimizer might decide against using the materialized view based on various factors, such as the freshness of the data, the size of the result set, the complexity of the query, and the availability of the materialized view2. Therefore, the answer is no, because the optimizer might decide against it.

NEW QUESTION # 76
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 cardinality of the stores to transaction count ratio is too low to use the STORE_ID as a clustering key.
B. The table is not big enough to take advantage of the clustering key.
C. Sales across stores are not uniformly distributed.
D. The STORE_ID should be numeric.

Answer: C

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 # 77
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