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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q206-Q211):

質問 # 206
You have a Snowflake table 'CUSTOMER ORDERS with columns 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. You need to calculate the cumulative sum of 'ORDER AMOUNT' for each customer, ordered by 'ORDER DATE. However, due to potential late-arriving data, you also need to implement a windowing function that resets the cumulative sum if there's a gap of more than 30 days between consecutive orders for a customer. Which SQL query best accomplishes this?

A. Option B
B. Option D
C. Option C
D. Option A
E. Option E

正解：C

解説：
Option C correctly uses a conditional partitioning approach. UG(ORDER DATE, 1, ORDER DATE) OVER (PARTITION BY CUSTOMER_ID ORDER BY ORDER_DATE)' calculates the previous order date for each customer. (ORDER_DATE - 1, ORDER DATE) OVER (PARTITION BY CUSTOMER ID ORDER BY ORDER DATE) > 30)' creates a boolean expression that is true when the difference between consecutive order dates exceeds 30 days. This boolean expression is then used as a secondary partition key, effectively restarting the cumulative sum whenever a gap of more than 30 days occurs. The primary partition is still 'CUSTOMER ID' , ensuring sums are calculated within each customer's order history. The ordering of 'ORDER_DATE is essential for the cumulative sum to be calculated chronologically.

質問 # 207
A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

A. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.
B. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.
C. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.
D. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.
E. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.

正解：D

解説：
Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.

質問 # 208
A data analyst is optimizing query performance for a large reporting dashboard that accesses a Snowflake table 'SALES DATA' with millions of rows. The dashboard includes several complex calculations and aggregations based on 'SALE DATE and 'PRODUCT ID' The analyst observes that the dashboard load time is unacceptably slow, even after implementing standard query optimization techniques. Considering Snowflake's caching mechanisms and query profile, which of the following actions would MOST effectively improve the dashboard's performance while minimizing cost?

A. Implement result caching by ensuring that the underlying queries are deterministic and have not been modified. No action is needed; Snowflake automatically manages result caching.
B. Create a materialized view that pre-calculates the aggregations needed by the dashboard. Refresh the materialized view periodically (e.g., daily) to maintain data freshness.
C. Increase the virtual warehouse size to a larger configuration (e.g., from X-Small to Large) to ensure sufficient compute resources. This directly speeds up individual query execution.
D. Implement query tags and monitor Snowflake query history using the 'QUERY HISTORY view to identify resource-intensive queries and optimize them using query rewriting or indexing techniques.
E. Partition the 'SALES_DATX table by 'SALE_DATE to reduce the amount of data scanned during query execution. This avoids unnecessary scans.

正解：B

解説：
Materialized views offer a significant performance boost by pre-calculating and storing the results of complex aggregations. This reduces the computational load during dashboard refreshes. While increasing virtual warehouse size (A) provides more resources, it's often more cost-effective to optimize queries. Result caching (B) is automatic but depends on query determinism and recent execution. Partitioning (D) is not directly applicable to Snowflake. Query tags and history (E) are helpful for analysis but don't directly speed up dashboard load times.

質問 # 209
A marketing team needs a daily report showing the conversion rate of leads to customers. They define conversion rate as (Number of Customers Acquired / Total Number of Leads) 100. The data resides in two tables: 'LEADS' and 'CUSTOMERS'. 'LEADS' contains all leads generated daily, and 'CUSTOMERS' contains all acquired customers, both tables having a 'LEAD_ID' and 'ACQUISITION DATE' field, with 'ACQUISITION DATE' being NULL in the 'LEADS' table. They want the report automated and delivered via email. Which combination of Snowflake features would BEST accomplish this task?

A. Create a Python UDF to calculate the conversion rate and create a scheduled task using it. Use Snowpark DataFrame APIs to read from and join the 'LEADS' and 'CUSTOMERS' tables, then use Snowflake email integration to deliver the report.
B. Create a dashboard using Snowsight connected to the 'LEADS' and 'CUSTOMERS tables. Schedule a daily email snapshot of the dashboard.
C. Create a stored procedure that calculates the conversion rate, then schedule a Snowflake task to execute the stored procedure daily. Use Snowflake's email integration to send the results.
D. Create a stream on both 'LEADS' and 'CUSTOMERS' tables. Use a task to monitor the stream and calculate the conversion rate, then email the results.
E. Create a view that calculates the conversion rate. Use a third-party ETL tool to extract data from the view daily and send an email report.

正解：C

解説：
Using a stored procedure and task with Snowflake's email integration offers the most native and efficient solution. It avoids the complexity of external tools or continuously monitoring streams. Snowsight dashboard snapshots might not be formatted as needed for a report. While Snowpark is viable, for a simple calculation and email report, a Stored Procedure is preferable. Views do not offer scheduling or emailing on their own.

質問 # 210
You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?

A. Create a zero-copy clone of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DEV'. Create a zero- copy clone of 'CUSTOMER_DATX named Update 'CUSTOMER_DATX with the cleaning logic. Point the downstream reporting to 'CUSTOMER DATA REPORTING'.
B. Create a zero-copy clone of named for testing. Create another zero-copy clone of 'CUSTOMER DATA DEV' named 'CUSTOMER DATA REPORTING'. Clean the data in 'CUSTOMER DATA DENT. Point downstream reporting to 'CUSTOMER DATA REPORTING'.
C. Create a zero-copy clone of 'CUSTOMER_DATA' named for testing. Clean the data in 'CUSTOMER_DATA_DEV'. Once satisfied, update the 'CUSTOMER_DATR table directly with the cleaning logic.
D. Create a zero-copy clone of named for testing. Clean the data in Create a separate table named 'CLEANED CUSTOMER DATA'. Insert the cleaned data from 'CUSTOMER DATA DEV' into the new 'CLEANED CUSTOMER DATA' table. Update with the cleaning logic.
E. Create a full copy of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DE-VS. Use a 'MERGE statement to update with the cleaned data from

正解：A

解説：
Option D is the most appropriate and safely covers all aspects. Cloning to lets you experiment with cleaning. The most important part of the question is to handle the downstream reporting. So cloning 'CUSTOMER DATA' to lets you test how your new updates will affect the reports that depend on the data. Updating the 'CUSTOMER_DATR with the cleaning logic lets you apply the tested data cleaning. The other options do not protect the production reporting from potentially breaking changes during the data cleaning process. They may also directly update the production data, increasing risk. In option B, even though you are pointing to the new cloned reporting table, since that is created from DEV table it will already have changed data, and we want to report on the original, not the one with the dev changes. Option E does not discuss downstream impact on the reports, so this is not fully addressing all the impacts.

質問 # 211
......

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DAA-C01試験の準備方法｜検証するDAA-C01難易度試験｜100％合格率のSnowPro Advanced: Data Analyst Certification Exam日本語解説集

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