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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which of the following statements are true regarding the use of user-defined functions (UDFs) in Snowflake to optimize query performance, especially when compared to equivalent SQL expressions? (Select all that apply)

A. UDFs can sometimes improve code readability by encapsulating complex logic, but this rarely translates into significant performance gains.
B. External functions and UDFs are the same, both reside outside snowflake.
C. SQL UDFs generally perform better than Java or Python UDFs because they are executed natively within the Snowflake engine.
D. UDFs can be useful when the same complex calculation needs to be performed multiple times within a single query, potentially reducing code duplication and improving maintainability, thereby indirectly improving performance by simplification.
E. Snowflake UDFs, especially those written in Java or Python, can introduce performance overhead due to the cost of invoking the external runtime environment.

Answer: A,C,D,E

Explanation:
UDFs can improve code readability but don't always translate to performance gains. Java and Python UDFs incur overhead because they run outside the Snowflake engine. SQL UDFs are generally faster. They are useful for code reuse. External functions and UDFs are not the same: UDFs reside inside snowflake.

NEW QUESTION # 21
You are responsible for maintaining a dashboard that visualizes sales data'. The dashboard relies on a Snowflake view named 'SALES SUMMARY. Users have reported slow refresh times, especially when filtering by date. The 'SALES SUMMARY view is defined as follows:

Which of the following actions could significantly improve the dashboard's performance when filtering by date, assuming 'SALES_TRANSACTIONS' table is large?

A. Create a materialized view on top of
B. Partition the 'SALES_TRANSACTIONS' table by the DATE column.
C. create a clustering key on the DATE column in the 'SALES_TRANSACTIONS' table.
D. Create a search optimization policy on the DATE column of the SALES_TRANSACTIONS table.
E. Add a 'ORDER BY DATE clause to the 'SALES SUMMARY' view definition.

Answer: C

Explanation:
Clustering the 'SALES_TRANSACTIONS table by the DATE column is the most effective solution. Snowflake uses micro- partitioning, and clustering helps to logically group similar data together within micro-partitions. When filtering by DATE, Snowflake can then efficiently prune micro-partitions that do not contain relevant data, significantly reducing the amount of data scanned. Materialized views (A) can help but need to be maintained and add complexity. Search optimization on DATE can help but it's not the intended use case, as DATE is a range based column (B). Ordering the results (C) doesn't affect query performance significantly. Partitioning is not directly supported in Snowflake. Clustering is the correct approach.

NEW QUESTION # 22
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 A
B. Option B
C. Option E
D. Option D
E. Option C

Answer: E

Explanation:
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.

NEW QUESTION # 23
You are analyzing customer order data in Snowflake. The 'orders' table has columns: and 'order_totar. Your task is to identify the top 5 customers who have consistently placed high-value orders over time. You need to rank customers based on their average order total, but only consider customers who have placed at least 10 orders. Furthermore, you want to account for the recency of orders by applying a weighted average where more recent orders contribute more to the average. Which of the following approaches will efficiently achieve this goal in Snowflake?

A. Calculate the average order total and order count for each customer using a subquery, then join the results with a generated series of dates to calculate the weighted average in the outer query, finally ranking the customers using DENSE_RANK().
B. Use a QUALIFY clause in conjunction with window functions to filter customers with at least 10 orders and calculate both average and weighted average. Then use the ranking function over the weighted average.
C. Calculate the average order total using AVG(), filter customers with COUNT( ) >= 10 using HAVING, then rank them using RANK() OVER (ORDER BY average_order_total DESC).
D. Create a stored procedure to iterate through each customer, calculate the weighted average order total, and then rank them in the application layer.
E. Use a weighted average calculation involving a date-based weighting factor (e.g., days since the order date), calculate the average order total with this weighting, filter with COUNT( ) >= 10 using HAVING, and then rank using RANK() OVER (ORDER BY weighted_average_order_total DESC).

Answer: B,E

Explanation:
Both options B and E correctly address the problem. B calculates a weighted average, filters based on the minimum order count, and then ranks customers based on the weighted average. E achieves the same result in using QUALIFY which is an important technique to filter. Option A doesn't account for weighting. C is inefficient and does not leverage Snowflake's processing power. D is unnecessarily complex with join, date series and subquery for simple operation that can be achieved using window functions.

NEW QUESTION # 24
A marketing team wants to visualize website traffic data in Snowsight. They have a table named 'WEBSITE TRAFFIC' with columns 'VISIT DATE' (DATE), 'PAGE URL' (VARCHAR), and 'VISITOR COUNT' (INTEGER). The team wants to create a line chart showing the daily visitor count for a specific page, filtered by date range, allowing users to dynamically select the page URL from a dropdown. Which of the following steps are MOST effective and correct to achieve this using Snowsight dashboards?

A. Write a Python script using the Snowflake Connector to query the data, process it using Pandas, and then generate a chart using Matplotlib. Embed the chart as an image in the Snowsight dashboard.
B. Create a view 'DAILY PAGE_VISITS using a SQL query like 'CREATE OR REPLACE VIEW DAILY PAGE_VISITS AS SELECT VISIT DATE, PAGE URL, AS TOTAL_VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT_DATE, PAGE_URL;'. Then, in Snowsight, create a dashboard and a line chart using this view with a filter on 'PAGE URL' and 'VISIT DATE', configured as dashboard variables.
C. Create a new chart in a Snowsight dashboard. Use a SQL query like 'SELECT VISIT_DATE, FROM WEBSITE_TRAFFIC WHERE PAGE URL = 'specific_page' AND VISIT DATE BETWEEN 'start_date' AND 'end_date' GROUP BY VISIT DATE ORDER BY VISIT DATE$. Manually update the query for each page.
D. Create a stored procedure in Snowflake that accepts 'start_date', 'end_date' , and as parameters, executes the query, and returns the result set. Call the stored procedure from the Snowsight dashboard, passing the filter values.
E. Create a materialized view using 'CREATE MATERIALIZED VIEW AS SELECT VISIT_DATE, PAGE_URL, AS TOTAL VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT DATE, PAGE URL;'. In Snowsight, create a dashboard and a line chart using this materialized view with a filter on and configured as dashboard variables.

Answer: B,E

Explanation:
Option B is the most effective because it creates a view that aggregates the data, and then utilizes Snowsight dashboard variables for filtering, providing a dynamic and user-friendly experience. Option D is also correct because using a materialized view will significantly improve the performance of the query, especially when dealing with large datasets. Materialized views pre-compute and store the results of the query, making it faster to retrieve the data for the dashboard. Option A is inefficient as it requires manual updates to the query. Option C introduces unnecessary complexity and doesn't leverage Snowflake's built-in capabilities. Option E is overkill and less efficient for this simple scenario.

NEW QUESTION # 25
......

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