Free Access Snowflake.ARA-C01.v2025-02-14.q177 Practice Test (Page 11)

Question 46

You ran the below query and it took a long time to run
select itembarcode from checkouts where BIBNUMBER = '2213435';
The clustering information shows the below result.
What can you derive from this information
select system$clustering_information('checkouts','(BIBNUMBER)');

A.The query is running slow because the warehouse does not have enough memory

B.The query is running slow because BIBNUMBER does not have an index created on it.

C.The data is not clustered well by BIBNUMBER and it is spread across all the micro-partitions, hence to retrieve a small number of micro-partitions, the query has to scan all the partitions in the table

Question 47

When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP () what will occur?

A.All rows loaded using a specific COPY statement will have varying timestamps based on when the rows were inserted.

B.Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were read from the source.

C.Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were created in the source.

D.All rows loaded using a specific COPY statement will have the same timestamp value.

Question 48

Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?

A.There were JSON nulls in the recent data imports.

B.The order of the keys in the JSON was changed.

C.The recent data imports contained fewer fields than usual.

D.There were variations in string lengths for the JSON values in the recent data imports.

Correct Answer: B

Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
* The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
* There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
* There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
* The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
References:
* Considerations for Semi-structured Data Stored in VARIANT
* Snowflake Architect Training
* Snowflake query performance on unique element in variant column
* Snowflake variant performance

Question 49

An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?

A.1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table
2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account

B.1) Enable replication for each database in the Production account
2) Create replica databases in the QA account
3) Create clones of the replica databases on a nightly basis
4) Run tests directly on those cloned databases

C.1) Create a share in the Production account for each database
2) Share access to the QA account as a Consumer
3) The QA account creates a database directly from each share
4) Create clones of those databases on a nightly basis
5) Run tests directly on those cloned databases

D.1) Create a stage in the Production account
2) Create a stage in the QA account that points to the same external object-storage location
3) Create a task that runs nightly to unload each table in the Production account into the stage
4) Use Snowpipe to populate the QA account

Question 50

Based on the Snowflake object hierarchy, what securable objects belong directly to a Snowflake account? (Select THREE).

A.Role

B.Table

C.Warehouse

D.Schema

E.Database

F.Stage

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Question 46

Question 47

Question 48

Question 49

Question 50

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