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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q306-Q311):

NEW QUESTION # 306
A Snowflake data warehouse has a large table named 'ORDERS' that is frequently queried by multiple virtual warehouses of different sizes for various analytical workloads. The data in the
'ORDERS' table is constantly being updated through batch loading processes. Which of the following strategies can be used to optimize query performance and minimize contention between different workloads, while ensuring data consistency?

A. Use Snowflake's caching mechanisms and query acceleration service to improve query performance.
B. Implement data partitioning on the 'ORDERS' table based on a frequently queried column to reduce the amount of data scanned by each query.
C. Create multiple materialized views on the 'ORDERS' table tailored to the specific query patterns of each virtual warehouse.
D. Utilize Snowflake's multi-cluster warehouses with auto-scaling enabled to handle concurrent queries from different workloads.
E. Create multiple clones of the 'ORDERS' table, one for each virtual warehouse, to isolate workloads and prevent contention.

Answer: A,C,D

Explanation:
Options B, D, and E are correct. Using caching and the query acceleration service (B) improves query performance by leveraging Snowflake's internal optimizations. Utilizing multi-cluster warehouses with auto-scaling (D) provides the necessary resources to handle concurrent queries without contention. Creating materialized views (E) tailored to specific query patterns pre- computes and stores the results, significantly reducing query execution time. Option A, cloning the table, consumes extra storage and isn't suitable for frequently updated data. Option C is an incorrect solution, as Snowflake does not directly support data partitioning by the user. Clustering can be used, but it isn't mentioned, and partitioning is wrong.

NEW QUESTION # 307
A data engineer needs to grant a group of analysts the ability to query data in the 'SALES DB.PUBLIC.ORDERS table, but prevent them from seeing any personally identifiable information (PII) in the 'CUSTOMER NAME column. Furthermore, the analysts should be able to create temporary tables based on the data. Which of the following steps would BEST accomplish this?

A. Grant the `SELECT privilege on the 'SALES DB.PUBLIC.ORDERS table to a custom role. Create a row access policy to filter data based on user roles. Grant `CREATE TEMPORARY TABLE on the database to the custom role.
B. Grant the `SELECT privilege on the 'SALES DB.PUBLIC.ORDERS table to a custom role, create a masking policy to redact the 'CUSTOMER NAME' column, and grant the 'APPLY MASKING POLICY privilege to the custom role. Grant 'CREATE TEMPORARY TABLE on the database to the custom role.
C. Grant the 'SELECT privilege on the 'SALES DB.PUBLIC.ORDERS table and 'CREATE TEMPORARY TABLE' on the database to the PUBLIC role. Use masking policies on the column.
D. Grant the `SELECT privilege on the 'SALES DB.PUBLIC.ORDERS table to a custom role. Create a secure view that excludes the `CUSTOMER NAME' column, granting `SELECT on the secure view to the role.
Grant 'CREATE TABLE on the database to the custom role.
E. Grant the `SELECT privilege on the 'SALES DB.PUBLIC.ORDERS table to a custom role, then create a view that excludes the 'CUSTOMER NAME' column, granting `SELECT on the view to the role. Grant 'CREATE TEMPORARY TABLE' on the database to the custom role.

Answer: E

Explanation:
Option A is the most secure and efficient. Granting 'SELECT on the base table and then creating a view ensures the analysts only see the data they are authorized to see. Secure views provide extra security by preventing unauthorized access even through side-channel attacks. Additionally,
'CREATE TEMPORARY TABLE grants the necessary permission for analysts to create temporary tables for their analysis. Masking policy requires the APPLY MASKING POLICY privilege which isn't a common practice for analysts, Option B uses PUBLIC role which is bad practice. Option D is incorrect because analysts need to create TEMPORARY tables, not persistent ones.

NEW QUESTION # 308
A data warehouse contains a table 'ORDERS' with columns 'ORDER ID', 'CUSTOMER ID',
'ORDER DATE, and `ORDER TOTAL' You need to optimize a query that frequently retrieves the total order amount for a specific customer within a given date range. Which of the following strategies would be MOST effective in improving query performance?

A. Increase the warehouse size to allow for more compute resources.
B. Create a materialized view that pre-calculates the total order amount for each customer.
C. create an index on the 'ORDER DATE column.
D. Create a view that filters the `ORDERS' table based on the date range.
E. Cluster the `ORDERS' table using `CUSTOMER ID and `ORDER DATES.

Answer: E

Explanation:
Clustering the table on `CUSTOMER ID and 'ORDER DATE is the most effective strategy because it physically organizes the data on disk based on these columns. This allows Snowflake to efficiently retrieve the relevant data for a specific customer and date range, minimizing the amount of data that needs to be scanned. Increasing warehouse size will improve general performance, but clustering optimizes for this specific query. Indexes are not typically used in Snowflake. A materialized view would be good, however, clustering is more effective.

NEW QUESTION # 309
You need to programmatically retrieve query history metadata (e.g., start time, end time, status, user) for queries executed in the last 24 hours using SQL from within Snowsight. However, you only want to retrieve records for queries that took longer than 5 seconds to execute, filtering by a specific user 'DATA USER'. Which of the following SQL statements, when executed in Snowsight, is the MOST efficient way to achieve this?

Answer: E

Explanation:
In the view is measured in milliseconds. Therefore, to filter for queries that took longer than 5 seconds, you need to use > 5000'. Options A and D are incorrect because `execution_status' and execution_time' are not valid columns. Option C calculates the difference manually, which, although functional, is less efficient than using the pre- calculated column. Option E uses an interval which is valid, but less performant and harder to read than the numerical comparision from option B. can be directly compared with the integer value representing miliseconds.

NEW QUESTION # 310
You are tasked with creating a table in Snowflake to store customer order data. You need to ensure that the 'order date' column always defaults to the current date if no value is provided during insertion. Additionally, you want to enable automatic clustering on the 'customer id' column to optimize query performance for order retrieval by customer. Which of the following SQL statements correctly date DATE DEFAULT CURRENT DATE) ORDER BY (customer_id); achieves this?

A. Option A
B. Option E
C. Option B
D. Option D
E. Option C

Answer: A

Explanation:
Option A is correct because it creates a table with a default value for 'order_date' using
'DEFAULT CURRENT_DATE and specifies clustering on 'customer_id' using `CLUSTER BY (customer_id)' during table creation. Snowflake supports specifying clustering keys during table creation. Option B is incorrect because 'ORDER BY' clause is used for ordering the data during query, not for clustering. Option C is also correct as the 'CLUSTER BY' can be done using ALTER statement as well . Option D is incorrect because Snowflake does not use traditional indexes and the syntax 'CREATE CLUSTERING INDEX is invalid. option E is incorrect because
'AUTOMATIC CLUSTERING BY' is not a valid syntax.

NEW QUESTION # 311
......

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