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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
A marketing company is analyzing customer purchase data stored in Snowflake to understand which customer demographics are most likely to purchase a newly launched product. The 'CUSTOMERS table has columns: 'customer_id', 'age' , 'gender' , 'location' , and 'household income'. The 'PURCHASES' table has columns: 'customer_id', 'purchase_date', and 'product id'. Which SQL query would most effectively identify the top three age groups with the highest purchase rate for the new product (product_id = 'NEW PRODUCT')?

Answer: C

Explanation:
Option D is the most effective because it calculates the purchase rate by dividing the count of distinct customers who purchased the new product by the total number of customers in each age group. This provides a normalized purchase rate for each age group, allowing for a fair comparison regardless of the size of each group. Options A and C only provide the raw count of purchases or unique purchasers, which doesn't account for the size of each age group. Option B uses 'SUM(CASE...V , which can work, but is less efficient than using 'COUNT(DISTINCTV in combination with a left join to account for all customers, even those who didn't make any purchases. Option E would produce an incorrect rate as it is dividing a customer's count by the distinct count of each customer, resulting in a rate close to 1.0 for each customer that made a purchase.


NEW QUESTION # 24
A telecommunications company wants to segment its customers based on their usage patterns for targeted marketing campaigns. You have access to a table named 'CUSTOMER USAGE with the following columns: 'CUSTOMER ONT), 'DATA USAGE GB' (FLOAT), 'VOICE CALL MINUTES (INT), and (INT). Which of the following Snowflake features or techniques would be MOST appropriate for performing customer segmentation and determining distinct customer clusters?

Answer: C,D

Explanation:
Options B and E are the most appropriate. Option B leverages Snowflake's UDF capabilities for in-database processing, allowing for potentially complex custom clustering algorithms. Option E allows integration with external machine learning platforms to take advantage of pre- built, optimized machine learning models. Option A is not appropriate because it just provides a count of distinct patterns, not the clustering itself. Option C is not scalable or maintainable for complex segmentation. Option D provides ranking, but not clustering or segmentation in the sense intended by the question.


NEW QUESTION # 25
You are loading data into a Snowflake table 'PRODUCT PRICES' using the COPY INTO command from a Parquet file stored in Azure Blob Storage. The 'PRODUCT PRICES' table has the following schema: VARCHAR, PRICE CURRENCY VARCHAR, LAST UPDATED TIMESTAMP NTZ. The Parquet file contains all these columns, but the 'LAST UPDATED column is stored as a Unix epoch timestamp (seconds since 1970-01-01 00:00:00 UTC). You need to transform the Unix epoch timestamp to a Snowflake TIMESTAMP NTZ during the data load. Which of the following options correctly demonstrates how to achieve this using a transformation within the COPY INTO command?

Answer: D

Explanation:
The correct answer is C. When using transformations with COPY INTO and an external stage, you must explicitly select the columns using '$1', '$2, etc. The gazure_stage/data.parquet' path is used in the 'FROM' clause, and converts the fourth column (LAST_UPDATED) from the Parquet file to a Snowflake TIMESTAMP. Note: It assumes column ordering in the Parquet file matches the order in the select statement. Using a SELECT statement within the FROM' clause is the correct approach when applying transformations directly during the COPY. The other options are incorrect because they either use the deprecated 'TRANSFORMATION' parameter (now replaced by select statements in the FROM clause) or attempt to apply transformations incorrectly. This is the only answer that is correct and will work. Options A, B, D, and E will all fail during the COPY INTO statement.


NEW QUESTION # 26
When connecting BI tools to Snowflake for dashboard creation, which factors must be considered to ensure effective integration? (Select all that apply)

Answer: A,C,D

Explanation:
Effective integration involves considering network latency, compatibility, and encryption requirements for Snowflake data.


NEW QUESTION # 27
In Snowflake, how does leveraging Parquet format contribute to optimizing query performance and storage efficiency?

Answer: B

Explanation:
Parquet format optimizes query performance and storage efficiency by enhancing data compression, reducing storage needs, and accelerating query execution in Snowflake.


NEW QUESTION # 28
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