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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?

Answer: A

Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.


NEW QUESTION # 35
You have a Snowflake table named 'customer transactionS with columns: 'customer id', 'transaction date', 'transaction amount, and product_categorV. You need to identify customers who have made purchases in more than three different product categories within the last 30 days. Which of the following Snowflake SQL queries is the MOST efficient and accurate way to achieve this, considering the large size of the table?

Answer: D

Explanation:
Option C is the most efficient. It directly groups by 'customer_id' and uses the 'HAVING' clause to filter customers who have purchased from more than three distinct product categories within the specified date range. Option A is functionally correct but less concise. Option B uses an inefficient subquery. Option D returns all customer IDs who purchased from any product category within the timeframe. Option E attempts to use window functions and ARRAY AGG, which is unnecessarily complex and less performant for this task.


NEW QUESTION # 36
A Data Analyst wants to use pandas code they have previously written to process a column of text and return multiple rows of parsed output for each input value. The Analyst wants to be able to join these results with other tables in a single transaction. Which type of extensibility feature should the Analyst use?

Answer: C

Explanation:
This scenario requires a specific combination of Snowflake's extensibility features: the ability to run Python (pandas) code, the ability to return multiple rows for a single input (tabular output), and high performance through vectorization.
A User-Defined Table Function (UDTF) is the correct architectural choice when an input value needs to be expanded into a set of rows. While a standard UDF returns exactly one value per input row, a UDTF can return zero, one, or many rows, making it ideal for "parsing" or "splitting" tasks. By using a Vectorized UDTF (specifically utilizing the Python handler with pandas), the Analyst can process blocks of rows as pandas DataFrames or Series. This is significantly more efficient than processing one row at a time because it reduces the overhead of the Snowflake-to-Python execution engine.
Evaluating the Options:
* Option A is incorrect because a standard UDF can only return a single scalar value per input row, making it impossible to "return multiple rows of parsed output."
* Option C is incorrect because External Functions are used to call code hosted outside of Snowflake (like an AWS Lambda). While they can process data, they introduce network latency and are not the native way to run pandas code for internal joins.
* Option D is incorrect because Stored Procedures are generally used for administrative tasks and procedural logic. While they can return tables, they cannot be directly "joined" with other tables in a standard SELECT statement in the same way a UDTF can.
* Option B is the correct choice. It satisfies the requirement for pandas integration, provides the necessary multi-row output via the process and end_partition methods, and allows the analyst to use the LATERAL keyword to join the parsed results directly with existing tables in a single SQL statement.


NEW QUESTION # 37
You have a 'PRODUCT SALES table with columns 'PRODUCT ID, 'SALE DATE, 'UNITS SOLD, and 'PRICE PER UNIT. You need to perform a complex data transformation for reporting purposes. Specifically, you need to calculate the 7-day moving average of the total revenue CUNITS SOLD 'PRICE PER UNIT) for each product. However, you also need to handle cases where data might be missing for some dates. For those missing dates within a 7-day window, you want to impute a zero value for 'UNITS SOLD before calculating the moving average. Which of the following approaches is MOST efficient and accurate in Snowflake to achieve this, assuming you want to minimize code complexity and execution time? (Choose all that apply)

Answer: D

Explanation:
Option B (using CTEs and a left join) is the most efficient and accurate approach. Here's why: CTEs provide modularity: They break down the complex transformation into logical steps, making the code easier to read and maintain. Date series generation: A CTE can generate a complete date series within the relevant range. This ensures that all dates within the 7-day window are considered. Left Join and Imputation: Left joining the generated date series with the 'PRODUCT _ SALES' table allows you to identify missing dates. Then, you can use 'COALESCE or similar functions to impute zero values for 'UNITS_SOLD' for these missing dates. Window Function: Finally, you can apply the 'AVG()' window function over the revenue (UNITS_SOLD PRICE_PER_UNIT), ordered by date, to calculate the 7-day moving average. Here's why the other options are less suitable: A (UDF): While UDFs are powerful, they can introduce performance overhead compared to native SQL operations. Imputing missing values and calculating a moving average are tasks that can be efficiently handled with SQL. C (Direct Window Function): This is incorrect as this won't handle missing dates and will lead to inaccurate moving averages. D (Time Travel): Time Travel is useful for data recovery and auditing, not for imputing missing data in a moving average calculation. E (Stored Procedure): Stored procedures with procedural logic are generally less efficient than set-based SQL operations, especially in Snowflake's columnar architecture. They also increase code complexity.


NEW QUESTION # 38
You're working with product catalog data in Snowflake. The product information is stored in a table named 'PRODUCTS' , and a key attribute, 'attributes' , contains a semi-structured JSON object for each product. This 'attributes' object can have varying keys, but you are interested in extracting specific keys and pivoting them into columns. The relevant JSON structure is as follows : { "color": "red", "size": "L", "material": "cotton", "style": "casual"} '"What method is the MOST efficient to transform this data to a relational structure, assuming you want to analyze product attributes such as 'color' and 'size' as separate columns?

Answer: B

Explanation:
Option B is the most efficient. Directly accessing the JSON elements using path accessors like allows Snowflake to optimize the query execution, which typically offers superior performance compared to flattening and pivoting with 'CASE statements. Flattening (Option A) introduces unnecessary complexity and overhead when specific attributes are known and desired. Options C and D are generally inefficient and should be avoided for this type of transformation. Creating a view is more performant and simple. Option E is overkill and introduces complexity that isn't needed since the required attributes are known.


NEW QUESTION # 39
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