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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Accounts and Security | 25% | - Governance and Compliance
|
| Topic 2: Data Engineering | 25% | - Data Transformation
|
| Topic 3: Snowflake Architecture | 30% | - Storage and Compute Design
|
| Topic 4: Performance Optimization | 20% | - Query Performance
|
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NEW QUESTION # 33
Arrange in order of performance(least to high)
1. External Table
2. External Table with partitioning
3. Materialized View Vs External table
4. Directly querying a file from the stage
Answer: C
NEW QUESTION # 34
The following table exists in the production database:
A regulatory requirement states that the company must mask the username for events that are older than six months based on the current date when the data is queried.
How can the requirement be met without duplicating the event data and making sure it is applied when creating views using the table or cloning the table?
Answer: D
Explanation:
A masking policy is a feature of Snowflake that allows masking sensitive data in query results based on the role of the user and the condition of the data. A masking policy can be applied to a column in a table or a view, and it can use another column in the same table or view as a conditional column. A conditional column is a column that determines whether the masking policy is applied or not based on its value1.
In this case, the requirement can be met by using a masking policy on the username column with event_timestamp as a conditional column. The masking policy can use a function that masks the username if the event_timestamp is older than six months based on the current date, and returns the original username otherwise. The masking policy can be applied to the user_events table, and it will also be applied when creating views using the table or cloning the table2.
The other options are not correct because:
A) Using a masking policy on the username column using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the masking policy function. This would add complexity and overhead to the masking policy, and it would not use the event_timestamp column as the condition for masking.
B) Using a row level policy on the user_events table using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the row access policy function. This would filter out the rows that have event_timestamp older than six months based on the valid dates, instead of masking the username column. This would not meet the requirement of masking the username, and it would also reduce the visibility of the event data.
D) Using a secure view on the user_events table using a case statement on the username column would require creating a view that uses a case expression to mask the username column based on the event_timestamp column. This would meet the requirement of masking the username, but it would not be applied when cloning the table. A secure view is a view that prevents the underlying data from being exposed by queries on the view. However, a secure view does not prevent the underlying data from being exposed by cloning the table3.
Reference:
1: Masking Policies | Snowflake Documentation
2: Using Conditional Columns in Masking Policies | Snowflake Documentation
3: Secure Views | Snowflake Documentation
NEW QUESTION # 35
What are purposes for creating a storage integration? (Choose three.)
Answer: A,B,C
NEW QUESTION # 36
A new table and streams are created with the following commands:
CREATE OR REPLACE TABLE LETTERS (ID INT, LETTER STRING) ;
CREATE OR REPLACE STREAM STREAM_1 ON TABLE LETTERS;
CREATE OR REPLACE STREAM STREAM_2 ON TABLE LETTERS APPEND_ONLY = TRUE;
The following operations are processed on the newly created table:
INSERT INTO LETTERS VALUES (1, 'A');
INSERT INTO LETTERS VALUES (2, 'B');
INSERT INTO LETTERS VALUES (3, 'C');
TRUNCATE TABLE LETTERS;
INSERT INTO LETTERS VALUES (4, 'D');
INSERT INTO LETTERS VALUES (5, 'E');
INSERT INTO LETTERS VALUES (6, 'F');
DELETE FROM LETTERS WHERE ID = 6;
What would be the output of the following SQL commands, in order?
SELECT COUNT (*) FROM STREAM_1;
SELECT COUNT (*) FROM STREAM_2;
Answer: B
Explanation:
In Snowflake, a stream records data manipulation language (DML) changes to its base table since the stream was created or last consumed. STREAM_1 will show all changes including the TRUNCATE operation, while STREAM_2, being APPEND_ONLY, will not show deletions like TRUNCATE. Therefore, STREAM_1 will count the three inserts, the TRUNCATE (counted as a single operation), and the subsequent two inserts before the delete, totaling 4. STREAM_2 will only count the three initial inserts and the two after the TRUNCATE, totaling 3, as it does not count the TRUNCATE or the delete operation.
References: The explanation is based on the Snowflake documentation on streams, which details how streams track changes and the difference between standard and APPEND_ONLY streams12.
NEW QUESTION # 37
What is a key consideration when setting up search optimization service for a table?
Answer: D
Explanation:
Search optimization service is a feature of Snowflake that can significantly improve the performance of certain types of lookup and analytical queries on tables. Search optimization service creates and maintains a persistent data structure called a search access path, which keeps track of which values of the table's columns might be found in each of its micro-partitions, allowing some micro-partitions to be skipped when scanning the table1.
Search optimization service can significantly improve query performance on partitioned external tables, which are tables that store data in external locations such as Amazon S3 or Google Cloud Storage. Partitioned external tables can leverage the search access path to prune the partitions that do not contain the relevant data, reducing the amount of data that needs to be scanned and transferred from the external location2.
The other options are not correct because:
* A. Search optimization service works best with a column that has a high cardinality, which means that the column has many distinct values. However, there is no specific minimum number of distinct values required for search optimization service to work effectively. The actual performance improvement depends on the selectivity of the queries and the distribution of the data1.
* C. Search optimization service does not help to optimize storage usage by compressing the data into a GZIP format. Search optimization service does not affect the storage format or compression of the data, which is determined by the file format options of the table. Search optimization service only creates an additional data structure that is stored separately from the table data1.
* D. The table does not need to be clustered with a key having multiple columns for effective search optimization. Clustering is a feature of Snowflake that allows ordering the data in a table or a partitioned external table based on one or more clustering keys. Clustering can improve the performance of queries that filter on the clustering keys, as it reduces the number of micro-partitions that need to be scanned. However, clustering is not required for search optimization service to work, as search optimization service can skip micro-partitions based on any column that has a search access path, regardless of the clustering key3.
References:
* 1: Search Optimization Service | Snowflake Documentation
* 2: Partitioned External Tables | Snowflake Documentation
* 3: Clustering Keys | Snowflake Documentation
NEW QUESTION # 38
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