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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) is a professional certification exam designed to validate the skills and knowledge of data architects and engineers who specialize in designing and implementing complex Snowflake cloud data platforms. SnowPro Advanced Architect Certification certification exam is recognized globally by companies that use Snowflake for their data management, warehousing, and analytics needs.
Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a cloud-based certification exam that is designed to validate the advanced skills and knowledge of Snowflake architects. SnowPro Advanced Architect Certification certification exam is intended for those professionals who possess a deep understanding of Snowflake data warehouses and their architecture, and who can design and implement complex Snowflake solutions using best practices. The SnowPro Advanced Architect Certification Exam is a vendor-neutral certification, which means that it is not affiliated with any particular vendor or technology.
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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a challenging and highly respected certification for professionals who work with the Snowflake cloud data platform. It is designed to test the expertise of architects who design and build complex data solutions on the Snowflake platform. By obtaining this certification, professionals can showcase their expertise in Snowflake's cloud data platform and gain a competitive edge in the industry.
NEW QUESTION # 216
While creating a clustering key, what is the recommendation for maximum number of columns that you can include as part of the key?
Answer: C
NEW QUESTION # 217
External functions must be scalar functions
Answer: A
NEW QUESTION # 218
When would you usually consider to add clustering key to a table
Answer: A,C
NEW QUESTION # 219
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?
Answer: B
Explanation:
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.
Considerations for Semi-structured Data Stored in VARIANT
Snowflake Architect Training
Snowflake query performance on unique element in variant column
Snowflake variant performance
NEW QUESTION # 220
JSON and PARQUET files can be loaded to columns in the same table
Answer: A
NEW QUESTION # 221
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