SOL-C01トレーニング費用、SOL-C01ソフトウエア

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Snowflake SOL-C01 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
トピック 2	Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.
トピック 3	Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.
トピック 4	Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.

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Snowflake Certified SnowPro Associate - Platform Certification 認定 SOL-C01 試験問題 (Q161-Q166):

質問 # 161
How can users filter queries in the Query History view in Snowsight?

A. By query status, date range, and user
B. By network configuration
C. By text-based search only
D. By hardware settings

正解：A

解説：
Snowsight's Query History view allows filtering queries bystatus,date range, andexecuting user. Additional filters include SQL text search, warehouse, duration, statement type, session ID, query ID, query tag, parameterized query hash, and whether a query was generated by a client or task. These filters help users identify long-running, failed, queued, or specific types of queries for troubleshooting and performance optimization. Network or hardware configurations have no role in Query History filtering.

質問 # 162
You are loading JSON data into Snowflake. The JSON data contains nested arrays and objects.
You need to flatten specific arrays while retaining other nested structures for analysis. Which combination of Snowflake functions and clauses would best achieve this?

A. Utilize `LATERAL FLATTEN' function with the 'PATH' parameter to target specific arrays for flattening, while allowing other nested structures to be retained as VARIANT columns. After flattening, use standard SQL queries to filter and transform the flattened data. You can also utilize
'OBJECT_CONSTRUCT in views if specific nested object structures need to be recreated for reporting.
B. Use `GET PATH' to extract specific elements from the JSON and load them directly into separate columns, avoiding flattening altogether.
C. Use `LATERAL FLATTEN' function with `PATH' parameter to specify the array to be flattened, and use 'OBJECT CONSTRUCT' to reconstruct desired nested objects after flattening.
D. Use FLATTEN' function with 'RECURSIVE = TRUE to flatten all nested structures indiscriminately.
E. Use only FLATTEN' function without specifying 'PATH' and then use 'RECURSIVE = FALSE

正解：A

解説：
Using 'LATERAL FLATTEN' with the 'PATH' parameter allows you to selectively flatten specific arrays within the JSON data while retaining other nested structures. After flattening, standard SQL queries are used to process the flattened data. 'OBJECT CONSTRUCT can be used later to reconstruct nested object in views if necessary.

質問 # 163
When unloading data from a Snowflake table, what is the target destination for the COPY INTO @stage command?

A. An internal or external stage
B. A local file system
C. Another Snowflake table
D. A data stream

正解：A

解説：
COPY INTO @stage writes query results or table data into files stored in either internal or external stages.
Unloading cannot target a local filesystem directly, cannot load into another table (use INSERT instead), and does not write to streams.

質問 # 164
You have a table 'PRODUCT_PRICES' defined as 'CREATE TABLE PRODUCT_PRICES (PRODUCT_ID INT, PRICE LAST UPDATED TIMESTAMP NTZ)'. You want to insert new prices for some products, but only if the new price is different from the existing price. If the price is the same, you want to update the 'LAST UPDATED' timestamp. Which of the following approaches would be the most efficient in Snowflake to achieve this?

A. Perform a 'SELECT statement for each 'PRODUCT_ID to check the existing price, then either
'INSERT or 'UPDATE accordingly.
B. Use a stored procedure that iterates through the new data and performs 'INSERT or 'UPDATE statements based on the existing price.
C. Use `MERGE statement to update the 'LAST UPDATED timestamp and insert new records if a record for the 'PRODUCT ID does not exist.
D. First 'INSERT all the new prices into a temporary table, then use a 'JOIN' with the original table to identify the rows that need to be updated or inserted.
E. Create a new table with all the product IDs that need to be updated and use 'INSERT OVERWRITE to replace original table.

正解：C

解説：
Option C is the most efficient because the 'MERGE statement is specifically designed for scenarios where you need to conditionally insert or update data based on a join condition. It avoids the overhead of multiple `SELECT statements or a stored procedure iterating through the data. A and B perform row-by- row operations which is slow in Snowflake. Although D is valid, it requires you to create temporary tables, populate and then join against the original table, which takes more time and code. Option E overwrites the whole table which is not needed since we only need to update records if a record for the 'PRODUCT_ID' does not exist.

質問 # 165
What information can be obtained by describing a table in Snowflake? (Choose any 3 options)

A. Indexes and keys
B. Table constraints
C. Table Origin
D. Column names and data types

正解：A、B、D

解説：
The DESCRIBE TABLE (or DESC TABLE) command in Snowflake provides core metadata about table structure. This includescolumn names,data types,nullability, and default values. It also returnsconstraintsif applied, such as PRIMARY KEY, UNIQUE, and CHECK constraints. Snowflake additionally displays information regarding clustering keys and other table characteristics. The command does not show table origin; Snowflake does not track lineage information directly through DESCRIBE TABLE. Indexes in Snowflake are not traditional B-tree indexes-Snowflake uses micro-partition pruning instead-but DESCRIBE TABLE can show clustering keys, which function similarly by enabling optimized data skipping.
Therefore, column definitions, constraints, and keys are all valid outputs of DESCRIBE TABLE.

質問 # 166
......

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SOL-C01トレーニング費用、SOL-C01ソフトウエア

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Snowflake SOL-C01 認定試験の出題範囲：

信頼的な-有効的なSOL-C01トレーニング費用試験-試験の準備方法SOL-C01ソフトウエア

Snowflake Certified SnowPro Associate - Platform Certification 認定 SOL-C01 試験問題 (Q161-Q166):

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