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Snowflake DAA-C01 Questions - DAA-C01 PDF Dumps [2025]

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q287-Q292):

NEW QUESTION # 287
A financial institution has implemented both Row Access Policies and Dynamic Data Masking. The Row Access Policy restricts access to transaction data based on the user's department (e.g., 'Fraud Detection', 'Compliance'). Dynamic Data Masking is applied to the 'ACCOUNT NUMBER column, masking all but the last four digits. The institution wants to build a report that shows the distribution of transaction amounts across different departments. Analysts in the 'Compliance' department need to be able to see the full, unmasked 'ACCOUNT NUMBER when investigating potential regulatory violations for transactions within their department only , while still adhering to the Row Access Policy. Which of the following approaches is the MOST secure and compliant way to implement this?

A. Create a stored procedure that executes with 'CALLER rights. Within the stored procedure, use a Snowflake Scripting block to check the user's role and department affiliation. If the user is in 'Compliance' and the transaction is within their department, retrieve the unmasked 'ACCOUNT_NUMBER directly from the underlying table; otherwise, retrieve the masked value. Grant the 'Compliance' role execute privilege on the stored procedure.
B. Create a custom role hierarchy. Create a role specifically for the 'Compliance' department that inherits from the existing role used by the Row Access Policy. Create a data masking policy based on this new role that unmasks the 'ACCOUNT NUMBER. Grant access to the report to this new role.
C. Create a view that joins the TRANSACTIONS' table with a 'DEPARTMENT_ACCESS' table, which contains mappings between departments and account numbers. Grant the 'Compliance' role 'SELECT' privilege on this view. The view will inherit the masking policy and row access policies, restricting data based on department and masking the account number for all departments except Compliance.
D. Modify the Dynamic Data Masking policy on the ' ACCOUNT_NUMBER' column to include a CASE statement that checks if the current user's role is 'Compliance' AND if the Row Access Policy allows access to the relevant department. If both conditions are true, the policy returns the unmasked 'ACCOUNT_NUMBER; otherwise, it applies the masking.
E. Grant the 'Compliance' role the global 'UNMASK' privilege. This will bypass the dynamic data masking policy on the 'ACCOUNT_NUMBER column whenever a Compliance user queries the 'TRANSACTIONS' table, while still respecting the Row Access Policy.

Answer: D

Explanation:
Option B is the most secure and compliant solution. By modifying the Dynamic Data Masking policy to include a CASE statement, you can conditionally unmask the 'ACCOUNT_NUMBER only when both the user is in the 'Compliance' department AND the Row Access Policy grants them access to the relevant transaction data. This ensures that the masking policy is only bypassed when absolutely necessary and that the Row Access Policy remains in effect. Option E is problematic because the UNMASK privilege is too broad and bypasses masking on all tables and columns. Using caller rights (option C) bypasses Row Access Policies too. Role hierarchies don't inherently affect masking. They change object privileges. Option A allows inheritance of the roles that the user is executing, however in the current context, the user will still get blocked due to ROW level security policy.

NEW QUESTION # 288
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?

A. Create a Snowflake Alert that triggers when the 'WEB ACTIVITY table is updated. The alert executes a stored procedure that queries the data, formats it as CSV using Snowflake Scripting, writes the CSV to an internal stage, and then uses a Python UDF to send the email with the CSV attachment. Grant the alert necessary privileges.
B. Create a Snowflake Stream on the 'WEB ACTIVITY table. When data changes are detected, trigger an external function (e.g., AWS Lambda) via a pipe. The Lambda function queries the data, formats it as CSV, uploads the file to S3, and sends an email with a download link.
C. Create a scheduled task in an external orchestrator (e.g., Airflow). This task uses the Snowflake Python connector to query the data, format it as CSV, writes the CSV to an external stage (e.g., AWS S3), and then uses an email service (e.g., AWS SES) to send the email with the CSV attachment. Configure appropriate IAM roles for Snowflake to access S3.
D. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format it as CSV using Javascript UDF, writes the CSV to an internal stage, and then uses a Java UDF (Util.EmailSender) to send the email with the CSV attachment. Grant necessary permissions to the task's service account to access the stage and execute the UDFs.
E. Use a third-party reporting tool (e.g., Tableau, Power Bl) to connect to Snowflake, create the report, and schedule it for daily email delivery with a CSV attachment. Configure the tool with the appropriate Snowflake connection details and credentials.

Answer: C

Explanation:
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.

NEW QUESTION # 289
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')

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

Answer: E

Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.

NEW QUESTION # 290
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. 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.
B. Create a view that encapsulates the 'WHERE' clause conditions (filtering by 'CUSTOMER ID and 'TRANSACTION DATE). Enable automatic query rewrite.
C. Cluster the 'CUSTOMER TRANSACTIONS' table by 'CUSTOMER and then 'TRANSACTION DATE. Create a materialized view preaggregating 'SUM(AMOUNT)' by PRODUCT_CATEGORY', and ' TRANSACTION_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. Create indexes on 'CUSTOMER and 'TRANSACTION DATE columns of the CUSTOMER TRANSACTIONS table.

Answer: C

Explanation:
Clustering 'CUSTOMER TRANSACTIONS' by 'CUSTOMER and 'TRANSACTION DATE improves data access performance by organizing data physically based on the frequently used filter criteria. Creating a materialized view that pre-aggregates the results by PRODUCT CATEGORY, 'CUSTOMER ID', and 'TRANSACTION DATE' allows Snowflake to serve the results directly from the materialized view, significantly reducing the compute cost and improving query performance. Snowflake does not use traditional indexes; clustering provides similar benefits.

NEW QUESTION # 291
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. Create a standard view using a JOIN between 'CUSTOMER DATA' and 'COUNTRY INFORMATION'. Refresh the view regularly using a scheduled task.
C. Use a User-Defined Function (UDF) to look up the currency code from the "COUNTRY_INFORMATION' table based on the customer's country code.
D. creates a materialized view with clustering enabled on the 'COUNTRY_CODE column after joining 'CUSTOMER_DATX and 'COUNTRY_INFORMATION'.
E. Create a secure view joining the two tables and granting access to users.

Answer: D

Explanation:
Materialized views generally provide better performance than standard views for complex queries, especially with joins. Clustering on the 'COUNTRY_CODE column further enhances performance by physically organizing the data based on this column, making lookups more efficient. Using secure view wouldn't impact on performance. Regular view refreshment using scheduled task is not as efficient compared to materialized view. UDFs can introduce performance overhead, especially for large datasets.

NEW QUESTION # 292
......

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