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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q228-Q233):

NEW QUESTION # 228
You are analyzing sales data for a retail company. The 'sales' table contains columns 'product id' (INT), 'sale date' (DATE), and 'sale_amount' (NUMBER). You need to calculate the percentage contribution of each product's sales to the total sales on each day. You want the result to include 'sale date', 'product_id', 'sale_amount', 'daily total' , and 'percentage_contribution'. Which of the following Snowflake queries achieves this correctly?

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

Answer: B,C

Explanation:
Options A and D are correct. Option A uses a window function OVER (PARTITION BY to calculate the total sales for each day. This is efficient because it calculates the total sales for each day in a single pass. Option D uses a subquery to calculate the total sales for each day and then joins this result back to the original table. Option B calculates a single total over the entire table, not by day. Option C calculates a single total sales amount over the entire table and assigns it to every row, which is incorrect. Option E orders the sales amount by the date but doesnt correctly partition by 'sale_date' leading to cumulative sum instead.

NEW QUESTION # 229
You are building a Snowsight dashboard that visualizes website traffic data'. The data includes a column 'visit_timestamp' (TIMESTAMP NTZ) and you need to display the number of unique visitors per hour for the last 24 hours. You also want to allow users to filter the data by country. You plan to use a chart to visualize the trend. Which of the following approaches are the MOST efficient and accurate for achieving this?

A. Use the visit_timestamp)' function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter directly in the SQL query.
B. Create a view that pre-aggregates the hourly visitor counts and includes the country. The dashboard queries this view and applies no additional filters.
C. Use the 'TO_CHAR(visit_timestamp, 'YYYY-MM-DD HH24')' function to group data by hour, but only use this option if 'visit_timestamp' is already stored as TEXT.
D. Create a calculated field in Snowsight that extracts the hour from the 'visit_timestamp' and then group by that calculated field to calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
E. Use the function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.

Answer: A,B

Explanation:
Option A is a good approach because 'DATE _ TRUNC is efficient for truncating timestamps, and applying the filter directly in the SQL query can optimize data retrieval. Option D is also very efficient, as it pre-aggregates the data, which improves dashboard performance. Option B is less efficient because the 'HOUR' function only returns the hour value without the date, making it harder to filter for last 24 hours. Also, dashboard-level filters can sometimes be less performant than SQL-level filters for large datasets. Option C introduces a calculated field, which is generally less efficient than performing the transformation directly in SQL. Option E is technically correct, it only applies to TEXT stored visit timestamp. Thus, pre-aggregation and 'DATE_TRUNC' are superior.

NEW QUESTION # 230
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?

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

Answer: B

Explanation:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.

NEW QUESTION # 231
You are tasked with building a dashboard in Looker Studio to visualize data from a Snowflake database. The data contains sensitive information, and the security team requires that only authorized users can access specific data based on their role. The Snowflake database has roles defined: 'ANALYST, 'MANAGER, and 'EXECUTIVE. The 'SALES DATA' table contains a 'REGION' column. 'ANALYST' should only see data for their assigned region, 'MANAGER should see data for their region and direct reports regions, and 'EXECUTIVE should see all regions. Which of the following is/are the MOST secure and efficient way(s) to implement row-level security in Snowflake and integrate it with Looker Studio without duplicating the data?

A. Implement Row Access Policies in Snowflake based on the USER NAME() or CURRENT ROLE() functions to filter the 'SALES DATA' table directly. Grant appropriate roles to users and connect Looker Studio using a service account that has the necessary privileges.
B. Create separate Snowflake views for each role, filtering the data based on the 'REGION' column. Connect Looker Studio to the appropriate view based on the user's role.
C. Create dynamic data masking policies in Snowflake to redact sensitive 'SALE_AMOUNT data based on roles and connect Looker Studio using a service account.
D. Implement a Snowflake stored procedure that accepts a user's role as input and returns the filtered data. Connect Looker Studio to this stored procedure.
E. Import the 'SALES DATA' table into Looker Studio and implement data blending with a user role mapping table to filter the data within Looker Studio.

Answer: A,C

Explanation:
Row Access Policies (RAP) in Snowflake provide the most secure and efficient way to implement row-level security. By defining policies based on the user's role, you can ensure that users only see the data they are authorized to access directly at the Snowflake level. Combining RAPs with dynamic data masking for 'SALE_AMOUNT adds an extra layer of security by redacting sensitive data based on the user's role. Option A requires managing multiple views, which can be cumbersome. Option B introduces complexity with stored procedures. Option D moves security logic into Looker Studio, which is less secure and can be bypassed. Using a service account with appropriate privileges ensures that Looker Studio can access the data securely and apply the defined Row Access Policies. Options C is correct because it keeps security logic within Snowflake. Option E is correct because it provides additional data masking depending on the security policies for sale amount.

NEW QUESTION # 232
You have a table named 'ORDERS with a 'ORDER DATE column stored as VARCHAR. The column contains dates in the format 'YYYY-MM-DD HH24:Ml:SS.FF3'. You need to calculate the number of orders placed in January 2023. Which of the following SQL statements will achieve this most efficiently and accurately, considering potential time zone differences configured at the user or account level?

Answer: A

Explanation:
Option E is the most robust because it explicitly handles the VARCHAR to TIMESTAMP conversion, incorporates timezone conversion from UTC to the session's timezone, and then extracts the DATE part for accurate filtering. This accounts for potential timezone discrepancies that might affect date boundaries. Options A and B rely on string manipulation, which is prone to errors and not recommended for date operations. Option C lacks time zone considerations, which can lead to issues. Option D is less efficient because MONTH() and YEAR() function needs to be applied, and it also lacks timezone conversion consideration.

NEW QUESTION # 233
......

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