SnowPro Advanced Certification DAA-C01 Exam Questions and Answers PDF

When working with very large tables, calculating the exact number of unique combinations of columns (cardinality) using COUNT(DISTINCT ...) is a resource-intensive operation. It requires the Snowflake query engine to keep an exhaustive list of every unique pair encountered in memory, which can lead to high credit consumption and performance bottlenecks.

To provide a "very quick estimate," Snowflake utilizes the HyperLogLog (HLL) algorithm. The function HLL(column1, column2, ...) returns an HLL state (a binary representation) that can be used to estimate the number of distinct values with a high degree of accuracy and minimal computational overhead. This is part of Snowflake's suite of approximate aggregation functions, which are essential for Data Analysis on massive datasets where a 1% margin of error is acceptable in exchange for significantly faster results.

Evaluating the Options:

Option A is syntactically incorrect; DISTINCT cannot be used in that position with COUNT.

Option C is a valid query but will be significantly slower and more expensive than an HLL estimate on a "very large table".

Option D simply counts the total number of non-null rows, which does not represent the "number of different values" (cardinality).

Option B is the 100% correct answer. It specifically addresses the requirement for a "quick estimate" using the industry-standard probabilistic counting method built into Snowflake.

While Snowflake provides various ways to encapsulate logic, Stored Procedures are the primary tool for procedural programming and dynamic SQL execution. Unlike standard UDFs, which are generally designed to calculate and return values within a query, stored procedures can execute administrative commands (DDL) and data manipulation (DML) that are not possible in a simple SELECT statement.

Using Snowflake Scripting (or languages like Python, Java, or JavaScript), an analyst can write a procedure that builds a SQL string as a variable and then executes it using the EXECUTE IMMEDIATE command. This is demonstrated in procedural logic flows where inputs determine specific execution paths. While Tasks can be used to schedule the execution of these procedures, the task itself is a scheduling agent and does not provide the logic-driven dynamic string construction inherent to the procedure.

To achieve the desired result, an analyst must understand the fundamental behavior of different JOIN types within Snowflake and how they affect the retention of records from the "left" or primary table. The goal here is to list all brokers, even those who have zero customers (like "Drew") or customers with zero orders (like "Debby").

In SQL, an INNER JOIN only returns rows when there is a match in both tables. If we were to use an INNER JOIN between BROKER and CUSTOMER, Drew would be excluded from the results because he has no associated records in the CUSTOMER table. Similarly, an INNER JOIN with the ORDERS table would exclude any broker whose customers haven't placed an order.

Evaluating the Join Logic:

Option C is the correct solution because it utilizes a chain of LEFT JOINs. A LEFT JOIN (or LEFT OUTER JOIN) ensures that every record from the left table (BROKER) is preserved in the result set. If no matching record exists in the joined table (CUSTOMER or ORDERS), Snowflake populates the columns with NULL. This is why "Drew" appears with a CUST_COUNT of 0 and "Debby" appears with a NULL for the total order amount.

Option A fails because it uses an INNER JOIN for the CUSTOMER table, which would immediately filter out "Drew."

Option B and Option D fail because they use INNER JOINs at different stages of the query, which would strip away brokers or customers that do not have matching order activity.

Additionally, the query correctly uses COUNT(DISTINCT c.customer_id) to ensure that customers are not double-counted if they have multiple orders, and GROUP BY 1 (referencing b.broker_name) to aggregate the data at the broker level. This pattern is essential for accurate Data Analysis in Snowflake when dealing with "optional" relationships in a star or snowflake schema.