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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A data analyst is working with a large table partitioned by (DATE type). The table contains millions of rows spanning several years. They need to optimize a query that retrieves sales data for a specific quarter of 2023. The initial query is: 'SELECT FROM sales_data WHERE EXTRACT(YEAR FROM sale_date) = 2023 AND EXTRACT(QUARTER FROM sale_date) = To improve performance using partition pruning, which of the following queries is the MOST efficient alternative?

A)

B)

C)

D)

E)

2. A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?

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

3. You are tasked with creating a dashboard to visualize website traffic data'. The data is stored in a Snowflake table 'WEB EVENTS' with the following columns: 'EVENT_ID', 'USER_ID', 'EVENT_TIMESTAMP', 'PAGE_URL', 'SESSION_ID', and 'DEVICE_TYPE. You need to calculate the average session duration for each device type, but the 'WEB EVENTS' table only contains individual events, not session start and end times. Assume a session is defined by a unique 'SESSION ID' for each 'USER ID. You are given a window function to calculate the start time and end time for a given 'SESSION ID': First_value and last_value. What steps are crucial to calculate average session duration by 'DEVICE TYPE?

A) Simply group by 'DEVICE_TYPE and calculate the average ' EVENT_TIMESTAMP'
B) Calculate the difference between the maximum and minimum 'EVENT _ TIMESTAMP' for each ' USER_ID. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.
C) Calculate the sum of all 'EVENT TIMESTAMP' values for each 'USER ID. Group by 'DEVICE_TYPE and calculate the average.
D) Use window functions to determine the start and end 'EVENT TIMESTAMP for each 'SESSION ID. Calculate the difference between these start and end times. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.
E) Calculate median of event timestamp using percentile_cont. The calculate difference between median and max timestamps.

4. You are tasked with creating a dashboard that displays the average transaction amount for each customer segment. However, sensitive customer information, such as credit card numbers, is stored in the 'TRANSACTIONS table and protected by Dynamic Data Masking. The masking policy replaces the credit card number with 'XXXX-XXXX-XXXX-XXXX'. The dashboard needs to allow analysts to drill down into individual transactions to identify fraud patterns, but without exposing the actual credit card numbers. Which of the following approaches is the MOST secure and efficient way to achieve this?

A) Create a tokenization service outside of Snowflake. Replace the credit card numbers in the 'TRANSACTIONS table with tokens. Store the mapping between tokens and credit card numbers securely. Provide the analysts with access to the tokenization service to de-tokenize the credit card numbers only when absolutely necessary for fraud investigation. The dashboard displays the tokenized values, and drill-down leads to a request to the tokenization service.
B) Create a view that selects all columns from the 'TRANSACTIONS table. Grant the 'ANALYST ROLE 'SELECT privilege on the view. The masking policy will automatically apply, preventing analysts from seeing the actual credit card numbers. Allow drill-down on all available fields.
C) Create a role hierarchy where the 'ANALYST ROLE inherits from a ROLE' that has the 'APPLY MASKING POLICY privilege on the column. Grant the 'ANALYST_ROLE 'SELECT privilege on the 'TRANSACTIONS' table. This allows drill-down while preserving the masked credit card numbers.
D) Create a stored procedure that executes with 'CALLER rights. The stored procedure queries the 'TRANSACTIONS' table and returns the data. Grant the 'ANALYST ROLE execute privilege on the stored procedure. This approach bypasses the masking policy but provides more control over which data is displayed during drill-down.
E) Create a UDF (User-Defined Function) that partially unmasks the credit card number, revealing only the last four digits. Apply this UDF in the dashboard when drilling down. This allows analysts to identify patterns while protecting most of the sensitive data.

5. While reviewing the query profile for a complex data transformation pipeline, you notice a significant amount of time spent in the 'Join' operation between two large tables, 'transactions' and 'customers'. The join is performed on the 'customer _ id' column. Which of the following are potential strategies to optimize the join performance?

A) Rewrite the query to use a 'LATERAL FLATTEN' function instead of a 'JOIN'.
B) Cluster both tables on the 'customer id' column.
C) Ensure that the 'customer_id' column has identical data types in both tables.
D) Increase the size of the virtual warehouse.
E) Broadcasting the smaller table to all compute nodes.

Solutions: