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Snowflake SnowPro Core Certification Exam Sample Questions (Q677-Q682):
NEW QUESTION # 677
A query containing a WHERE clause is running longer than expected. The Query Profile shows that all micro-partitions being scanned How should this query be optimized?
- A. Add a clustering key to the table
- B. Add a Dynamic Data Masking policy to the table.
- C. Add a limit clause to the query.
- D. Create a view on the table.
Answer: A
Explanation:
When a query containing a WHERE clause is running longer than expected, and the Query Profile shows that all micro-partitions are being scanned, the query can be optimized by adding a clustering key to the table.
Understanding Micro-Partitioning in Snowflake:
Snowflake automatically partitions tables into micro-partitions for efficient storage and query performance.
Each micro-partition contains metadata about the range of values it holds, which helps in pruning irrelevant partitions during query execution.
Role of Clustering Keys:
A clustering key defines how data in a table is organized within micro-partitions.
By specifying a clustering key, you can control the physical layout of data, ensuring that related rows are stored together.
This organization improves query performance by reducing the number of micro-partitions that need to be scanned.
Optimizing Queries with Clustering Keys:
Adding a clustering key based on columns frequently used in WHERE clauses helps Snowflake quickly locate and scan relevant micro-partitions.
This minimizes the amount of data scanned and reduces query execution time.
Example:
ALTER TABLE my_table CLUSTER BY (column1, column2);
This command adds a clustering key to my_table using column1 and column2.
Future queries that filter on these columns will benefit from improved performance.
Benefits:
Reduced query execution time: Fewer micro-partitions need to be scanned.
Improved resource utilization: More efficient data retrieval leads to lower compute costs.
Reference:
Snowflake Documentation: Clustering Keys
Snowflake Documentation: Query Profile
NEW QUESTION # 678
What actions can be performed by a consumer account on a shared database? (Select TWO)
- A. Joining the data from a shared table with another table
- B. Cloning a shared table
- C. Executing the select statement on a shared table
- D. Using Time Travel on a shared table
- E. Modifying the data in a shared table
Answer: A,C
NEW QUESTION # 679
What computer language can be selected when creating User-Defined Functions (UDFs) using the Snowpark API?
- A. SQL
- B. Swift
- C. Python
- D. JavaScript
Answer: C
NEW QUESTION # 680
Which Snowflake data type is used to store JSON key value pairs?
- A. BINARY
- B. VARIANT
- C. TEXT
- D. STRING
Answer: B
Explanation:
The VARIANT data type in Snowflake is used to store JSON key-value pairs along with other semi-structured data formats like AVRO, BSON, and XML. The VARIANT data type allows for flexible and dynamic data structures within a single column, accommodating complex and nested data. This data type is crucial for handling semi-structured data in Snowflake, enabling users to perform SQL operations on JSON objects and arrays directly.
Reference:
Snowflake Documentation: Semi-structured Data Types
NEW QUESTION # 681
What is the purpose of using the OBJECT_CONSTRUCT function with me COPY INTO command?
- A. Reorder the data columns according to a target table definition and then unload the rows into the table.
- B. Convert the rows in a relational table lo a single VARIANT column and then unload the rows into a file.
- C. Convert the rows in a source file to a single variant column and then load the rows from the file to a variant table.
- D. Reorder the rows in a relational table and then unload the rows into a file
Answer: B
Explanation:
The OBJECT_CONSTRUCT function is used with the COPY INTO command to convert the rows in a relational table to a single VARIANT column, which can then be unloaded into a file. This is useful for transforming table data into a semi-structured JSON format
NEW QUESTION # 682
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