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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
You are tasked with creating a dashboard to visualize customer churn. You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID, 'JOIN DATE, 'LAST ACTIVE DATE, 'REVENUE', and CHURNED' (BOOLEAN). You want to present a cohort analysis showing the retention rate of customers over time, grouped by their join month. Which of the following approaches using SQL and visualization techniques is the MOST effective for creating this cohort analysis visualization for a dashboard using only snowflake?

A. Create a series of SQL queries, one for each cohort, to calculate the retention rate for each subsequent month. Combine the results from these queries in your dashboard and display them using a line chart.
B. Export the data to a BI tool to make a visualization.
C. Create a stored procedure that iterates through each join month, calculates the retention rate for each subsequent month, and stores the results in a temporary table. Then, use a charting library integrated with your dashboard to visualize the data from the temporary table as a heatmap or retention table.
D. Use a sequence to generate a series of dates representing the months since joining, then use conditional aggregation to calculate the number of customers retained in each month for each cohort. Visualize this data using a line chart or heatmap.
E. Create a single SQL query that calculates the retention rate for each cohort (join month) using window functions to count active customers in each subsequent month. Then, display the results in a bar chart showing the retention rate for each cohort over time.

Answer: D

Explanation:
Option C is the most efficient and scalable. Using a sequence and conditional aggregation allows calculating the retention rate for each cohort in a single SQL query. This avoids the overhead of stored procedures (B) or multiple queries (D). A line chart or heatmap provides an effective visualization of cohort retention over time. Option A can become complex to manage, especially when handling larger datasets. Finally, exporting the data for external visualization defeats the purpose of Snowflake analysis. Sequences and conditionals are preferred as Snowflake doesn't natively support loops like stored procedures for cohort analysis.

NEW QUESTION # 101
A data analyst is using a data feed from the Snowflake Marketplace which delivers data in JSON format. They need to create a relational table to store this data, but the JSON structure is deeply nested and contains arrays. Which of the following approaches would be the MOST efficient way to create the table and load the JSON data, taking into consideration performance and minimizing data duplication?
(Choose two)

A. Create a view on top of the JSON data stored in a single VARIANT column, using JSON path expressions to extract specific fields.
B. Load the JSON data into a staging table, then use a series of INSERT statements with JSON parsing functions to populate the final relational table.
C. Use a stored procedure to parse the JSON data and insert it into a pre-defined relational table.
D. Create multiple relational tables, flattening the JSON structure using LATERAL FLATTEN and storing related data in separate tables.
E. Create a single table with a VARIANT column to store the entire JSON document.

Answer: B,D

Explanation:
Options B and E are the most efficient. Option B: Using 'LATERAL FLATTEN' allows you to break down the JSON structure into relational tables, minimizing data duplication and improving query performance for specific fields. Option E: Loading the JSON data into a staging table and then using INSERT statements with JSON parsing functions provides a controlled and efficient way to transform and load the data into the final relational table. Option A: While storing the entire JSON in a VARIANT column is easy, it can lead to performance issues for queries that need to access specific fields within the JSON. Option C: Stored procedures can be complex and less efficient than using native Snowflake JSON parsing functions. Option D: Creating views on VARIANT columns can still suffer from performance issues compared to flattened relational tables.

NEW QUESTION # 102
You are tasked with cleaning a dataset containing customer information. One of the columns, 'PHONE NUMBER', contains inconsistent data: some entries have leading country codes (+1), some have dashes, and others have no separators at all. You need to create a function that standardizes all phone numbers to a consistent format (e.g., '1234567890'). Which of the following Snowflake UDFs (User- Defined Functions) effectively cleans and standardizes the phone numbers, removing all non-numeric characters and country codes?

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

Answer: B,D

Explanation:
Option A and E are correct. Both use REGEXP REPLACE to remove any non-numeric characters (to-9]+). Then, they use REGEXP_REPLACE again to remove the leading country code (+1 or 1). The other options are either too simplistic (only handling specific characters) or don't fully remove the country code reliably. Option A removes leading '1+' and Option E removes '+1'. The '+ sign needs to be escaped in the regex pattern.

NEW QUESTION # 103
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?

Answer: D

Explanation:
Option A is the most efficient because it directly uses the 'sale_date' column with a 'BETWEEN' clause using specific date values. This allows Snowflake to directly leverage the partition pruning based on the date range. Options B and C use functions C YEAR, 'QUARTER on the 'sale_date' column, preventing efficient partition pruning. Option D uses 'LIKE', which is not suitable for date comparisons and would likely result in a full table scan, furthermore 'LIKE operator will not work with Date Data type. Option E does not prune to a specific quarter.

NEW QUESTION # 104
A data analyst is tasked with loading data into a Snowflake table 'ORDERS' with the following structure: 'CREATE TABLE ORDERS ( ORDER ID INT, CUSTOMER ID INT, ORDER DATE DATE, TOTAL_AMOUNT The data analyst needs to ensure that 'ORDER ID' is unique and not null, 'CUSTOMER ID' references a valid customer in the 'CUSTOMERS' table (column name 'CUSTOMER ID'), and 'ORDER DATE' is not in the future. Which of the following combination of constraints is the most efficient and appropriate way to enforce these Fules in Snowflake? 'CREATE TABLE CUSTOMERS ( CUSTOMER ID INT PRIMARY KEY, CUSTOMER_NAME VARCHAR(255));'.

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

Answer: D

Explanation:
Option E is the most appropriate and efficient. Using a PRIMARY KEY implies an index which while beneficial for joins isn't necessary if simple uniqueness and not null are the primary requirement. A CHECK constraint 'ORDER_DATE <= is the best way to prevent future dates as TRIGGER is not available and view doesn't prevent data from being ingested.

NEW QUESTION # 105
......

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