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| Section | Objectives |
|---|---|
| Performance and Optimization | - Query performance tuning
|
| Data Sharing and Collaboration | - Secure data sharing
|
| Data Pipelines and Data Engineering | - Data ingestion and transformation
|
| Security, Governance, and Compliance | - Data governance
|
| Data Architecture and Design | - Data modeling and schema design
|
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NEW QUESTION # 62
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?
Answer: D
Explanation:
According to the Snowflake documentation, 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(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source. References:
* Snowflake Documentation: Loading Data into Tables with Default Values
* Snowflake Documentation: COPY INTO table
NEW QUESTION # 63
Two queries are run on the customer_address table:
create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0),
CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME
VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE VARCHAR(20) ); ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID); Which queries will benefit from the use of the search optimization service? (Select TWO).
Answer: B,C
Explanation:
The use of the search optimization service in Snowflake is particularly effective when queries involve operations that match exact substrings or start from the beginning of a string. The ALTER TABLE command adding search optimization specifically for substrings on the CA_ADDRESS_ID field allows the service to create an optimized search path for queries using substring matches.
* Option A benefits because it directly matches a substring from the start of the CA_ADDRESS_ID, aligning with the optimization's capability to quickly locate records based on the beginning segments of strings.
* Option B also benefits, despite performing a full equality check, because it essentially compares the full length of CA_ADDRESS_ID to a substring, which can leverage the substring index for efficient
* retrieval.Options C, D, and E involve patterns that do not start from the beginning of the string or use negations, which are not optimized by the search optimization service configured for starting substring matches.References: Snowflake's documentation on the use of search optimization for substring matching in SQL queries.
NEW QUESTION # 64
You have set time-travel retention to 10 days. You now increase the retention period by 10 more days to make it 20 days.
What will be the impact on the table data?
Answer: A,B
NEW QUESTION # 65
A Snowflake Architect is designing a multiple-account design strategy.
This strategy will be MOST cost-effective with which scenarios? (Select TWO).
Answer: D,E
NEW QUESTION # 66
An Architect needs to define a table structure for an unfamiliar semi-structured data set. The Architect wants to identify a list of distinct key names present in the semi-structured objects.
What function should be used?
Answer: D
Explanation:
When working with unfamiliar semi-structured data such as JSON, a common first step is to explore its structure and identify all possible keys. Snowflake's FLATTEN function is specifically designed to explode VARIANT, OBJECT, or ARRAY data into relational form. Using the RECURSIVE option allows FLATTEN to traverse nested objects and arrays, returning all nested keys regardless of depth (Answer A).
This approach enables architects to query and aggregate distinct key names, making it ideal for schema discovery and exploratory analysis. INFER_SCHEMA, by contrast, is used primarily with staged files to infer column definitions for external tables or COPY operations, not for exploring existing VARIANT data already stored in tables. PARSE_JSON simply converts a string into a VARIANT type and does not help identify keys. RESULT_SCAN is used to query the results of a previously executed query and is unrelated to schema discovery.
For SnowPro Architect candidates, this highlights an important semi-structured data design pattern: using FLATTEN (often with RECURSIVE) to explore, profile, and understand evolving data structures before committing to a relational schema or transformation pipeline.
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NEW QUESTION # 67
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