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最新的 SnowPro Advanced DAA-C01 免費考試真題:

1. You have a Snowflake table called 'CUSTOMER ORDERS that stores customer order data'. The business requires you to generate a weekly report on the top 10 customers by order value, delivered as an Excel file to a shared network drive. The network drive is accessible by a service account that your Snowflake account can authenticate against. The report must include customer name, total order value, and number of orders. Which approach is the MOST secure and efficient for automating this process?

A) Leverage a Snowflake Task to run a stored procedure. The procedure queries the data, transforms it into CSV format using Snowflake scripting. Then uses a Java UDF to copy the CSV to an internal stage, from where a separate process (outside Snowflake) monitors for new files and transfers them to the network drive using the service account. Securely manage credentials for both the Java UDF and the external process.
B) Create a Snowflake external function using AWS API Gateway and AWS Lambda. The external function queries the data from Snowflake, formats it as an Excel file using a Python library (e.g., openpyxl) within the Lambda function, and saves the file directly to the network drive using the service account's credentials. Configure API Gateway to authenticate requests from Snowflake.
C) Create a view on top of the CUSTOMER_ORDERS table that calculates the required metrics. Use a third-party ETL tool to extract the data from the view, format it as an Excel file, and save it to the network drive. Configure the ETL tool with appropriate Snowflake credentials.
D) Use a Snowflake Task to trigger a Snowpipe. A Snowflake stored procedure that executes SQL code to query for relevant data, convert it to JSON, then the Snowpipe load into the network directory using REST API. Grant necessary permissions to the task's service account.
E) Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format the data using Javascript UDF to XML, write the Excel file to an internal stage using Java UDF, and then use a Python UDF to copy the file to the network drive. Grant necessary permissions to the task's service account.

2. A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)

A)

B)

C)

D)

E)

3. A Snowflake data warehouse contains a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID, 'TRANSACTION DATE', 'AMOUNT', and 'PRODUCT CATEGORY'. Analysts frequently run queries that aggregate transaction amounts by product category for specific customer segments. The following query pattern is common:

Which of the following strategies, when implemented together, would BEST optimize the performance of this query pattern, considering both result caching and data access patterns?

A) Create indexes on 'CUSTOMER and 'TRANSACTION DATE columns of the CUSTOMER TRANSACTIONS table.
B) Create a view that encapsulates the 'WHERE' clause conditions (filtering by 'CUSTOMER ID and 'TRANSACTION DATE). Enable automatic query rewrite.
C) Tune the virtual warehouse size to be as small as possible while still meeting performance requirements. Ensure the statistics on the table are up to date.
D) create a materialized view that pre-aggregates 'SUM(AMOUNT)' by 'PRODUCT_CATEGORY, 'CUSTOMER_ID , and 'TRANSACTION_DATE. Regularly refresh the materialized view.
E) Cluster the 'CUSTOMER TRANSACTIONS' table by 'CUSTOMER and then 'TRANSACTION DATE. Create a materialized view preaggregating 'SUM(AMOUNT)' by PRODUCT_CATEGORY', and ' TRANSACTION_DATE

4. You are tasked with enriching a customer dataset in Snowflake. The 'CUSTOMER DATA table contains customer IDs and country codes. You have a separate 'COUNTRY INFORMATION' table that contains country codes and corresponding currency codes. Both tables reside in the 'RAW DATA schema of the 'ANALYTICS DB' database. You need to create a view called ENRICHED CUSTOMER DATA' in the 'TRANSFORMED DATA' schema that joins these tables to add currency information to the customer data'. You want to optimize this view for performance. Which of the following approaches would be the MOST efficient and scalable, considering potential data volume increases?

A) Create a materialized view using a simple JOIN between 'CUSTOMER_DATA' and 'COUNTRY_INFORMATION'.
B) creates a materialized view with clustering enabled on the 'COUNTRY_CODE column after joining 'CUSTOMER_DATX and 'COUNTRY_INFORMATION'.
C) Create a standard view using a JOIN between 'CUSTOMER DATA' and 'COUNTRY INFORMATION'. Refresh the view regularly using a scheduled task.
D) Use a User-Defined Function (UDF) to look up the currency code from the "COUNTRY_INFORMATION' table based on the customer's country code.
E) Create a secure view joining the two tables and granting access to users.

5. You have a requirement to load semi-structured JSON data from an internal stage into a Snowflake table. The JSON data contains nested arrays and objects. You need to flatten specific elements from these nested structures into separate columns in the target table during the load process. Assuming the internal stage is already configured, which of the following 'COPY INTO' statement snippets would correctly extract and load the 'city' from the 'address' object within the JSON, and the first element (index 0) of the phoneNumbers' array into corresponding columns in the target table?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

問題與答案：