A new user user_01 is created within Snowflake. The following two commands are executed:
Command 1-> show grants to user user_01;
Command 2 ~> show grants on user user 01;
What inferences can be made about these commands?
Therefore, the correct inference is that command 1 defines all the grants which are given to user_01, and command 2 defines which role owns user_01.
A Developer is having a performance issue with a Snowflake query. The query receives up to 10 different values for one parameter and then performs an aggregation over the majority of a fact table. It then
joins against a smaller dimension table. This parameter value is selected by the different query users when they execute it during business hours. Both the fact and dimension tables are loaded with new data in an overnight import process.
On a Small or Medium-sized virtual warehouse, the query performs slowly. Performance is acceptable on a size Large or bigger warehouse. However, there is no budget to increase costs. The Developer
needs a recommendation that does not increase compute costs to run this query.
What should the Architect recommend?
Enabling the search optimization service on the table can improve the performance of queries that have selective filtering criteria, which seems to be the case here. This service optimizes the execution of queries by creating a persistent data structure called a search access path, which allows some micro-partitions to be skipped during the scanning process. This can significantly speed up query performance without increasing compute costs1.
Reference
* Snowflake Documentation on Search Optimization Service1.
An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO
Which configuration is valid?
For the configuration of data unloading in Snowflake, the valid option among the provided choices is 'C.' This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO <location> command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake's capabilities for data unloading to cloud storage platforms. Reference:
Snowflake Documentation on COPY INTO command
Snowflake Documentation on Supported File Formats
Snowflake Documentation on Compression and Encoding Options
The data share exists between a data provider account and a data consumer account. Five tables from the provider account are being shared with the consumer account. The consumer role has been granted the imported privileges privilege.
What will happen to the consumer account if a new table (table_6) is added to the provider schema?
When a new table (table_6) is added to a schema in the provider's account that is part of a data share, the consumer will not automatically see the new table. The consumer will only be able to access the new table once the appropriate privileges are granted by the provider. The correct process, as outlined in option D, involves using the provider's ACCOUNTADMIN role to grant USAGE privileges on the database and schema, followed by SELECT privileges on the new table, specifically to the share that includes the consumer's database. This ensures that the consumer account can access the new table under the established data sharing setup. Reference:
Snowflake Documentation on Managing Access Control
Snowflake Documentation on Data Sharing
A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is configured to load data into staging database tables. Then a task runs to load the data from the staging database tables into the reporting database tables.
The company is satisfied with the availability of the data in the reporting database tables, but the reporting tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of the tables in the reporting database.
What step can be taken to improve the pruning of the reporting tables?
Effective pruning in Snowflake relies on the organization of data within micro-partitions. By using an ORDER BY clause with clustering keys when loading data into the reporting tables, Snowflake can better organize the data within micro-partitions. This organization allows Snowflake to skip over irrelevant micro-partitions during a query, thus improving query performance and reducing the amount of data scanned12.
Reference =
* Snowflake Documentation on micro-partitions and data clustering2
* Community article on recognizing unsatisfactory pruning and improving it1
Joseph
2 days agoMicheline
9 days agoJani
16 days agoMalinda
1 months agoErinn
1 months agoMari
1 months agoMozelle
2 months agoDeangelo
2 months agoSamira
2 months agoTy
2 months agoSheldon
3 months agoMinna
3 months agoTawna
3 months agoMerilyn
3 months agoKimbery
4 months agoVonda
4 months agoGlory
4 months agoDalene
4 months agoEliz
4 months agoSherly
5 months agoCarman
5 months agoHortencia
5 months agoJamie
5 months agoAlverta
6 months agoRory
6 months agoBev
6 months agoErasmo
6 months agoPrincess
7 months agoAnnamae
7 months agoFernanda
7 months agoGalen
7 months agoGlenn
8 months agoBernardine
9 months agoAshley
9 months agoLeoma
9 months agoJerry
9 months agoHerminia
9 months agoEarlean
11 months agoBrianne
11 months ago