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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?

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

Answer: C

Explanation:
Option B is the correct answer. The 'PARTITION BY account_id' clause ensures that the moving average is calculated separately for each account. The 'ORDER BY transaction_date ASCS clause orders the transactions within each account by date. The 'ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING' clause defines the window frame as the current row, the 3 preceding rows, and the 3 following rows based on row number (not date ranges). Option A doesn't partition by account_id, so the moving average will be across all accounts which is not what we want. Option C uses 'RANGE instead of which relies on finding all entries within that date range, that may have many entries making the numbers incorrect. Option D only considers preceding rows, not following ones. Option E doesn't have any windowing and therefore the data is calculated to the current point, without consideration for following or previous days.

NEW QUESTION # 72
You are tasked with retrieving data from a source system that outputs a large stream of semi-structured JSON data'. The data contains nested arrays and deeply nested objects. The data needs to be transformed before being loaded into Snowflake to flatten the structure and extract relevant fields. Which approach is most efficient and scalable for retrieving and preparing this data?

A. Use a Snowpipe with a SnowSQL transformation script to flatten and extract the required fields during the COPY INTO process.
B. Create an external table pointing to the source data and query it directly. Use Snowflake's JSON functions in the query to extract the relevant fields.
C. Create a Snowpark Python UDF to parse and flatten the JSON data during the COPY INTO process. Pass each JSON record as input to the UDF.
D. Load the raw JSON data into a VARIANT column in Snowflake using COPY INTO, then use Snowflake's JSON functions to transform the data within a VIEW.
E. Implement a data pipeline using an external stream processing framework (e.g., Apache Kafka, AWS Kinesis) to transform and flatten the JSON data before loading it into Snowflake using Snowpipe.

Answer: E

Explanation:
Option C is the most efficient and scalable. Using an external stream processing framework allows for real-time or near real-time transformation and flattening of the JSON data before it even reaches Snowflake, offloading the processing burden. Options A and B might struggle with very large and complex JSON structures. The SnowSQL script in option A will be complex and less efficient. Transforming within a view in option B delays the transformation and can impact query performance. Option D might face performance issues as passing each record to a Python UDF can be overhead heavy. External tables (option E) are generally best for read-only access and might not be suitable for complex transformations.

NEW QUESTION # 73
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')

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

Answer: D

Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.

NEW QUESTION # 74
A table named 'website_visits' tracks user activity with columns 'user_id', 'visit_timestamp' , and 'page_view'. You need to determine the time difference (in minutes) between each user's consecutive page views. Assuming 'visit_timestamp' is of data type TIMESTAMP NTZ, which Snowflake SQL query will accurately calculate this?

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

Answer: C

Explanation:
Option B is correct. start_time, end_timey calculates the difference in minutes between two timestamps. 1, visit_timestamp) OVER (PARTITION BY user_id ORDER BY visit_timestampy retrieves the previous timestamp for each user. The default value is crucial to handle the first visit for each user. Option A is incorrect because subtracting timestamps directly results in a value in days, not minutes. Option C is incorrect because 'TIMEDIFF does not exist in Snowflake. Option D uses 'TIMESTAMPDIFF but it does not accept the time unit as the first parameter. Option E uses AVG aggregation which is not the purpose of the window functions in this scenario.

NEW QUESTION # 75
You are building a sales performance dashboard in Snowflake for a retail company. The data includes sales transactions, product information, and customer demographics. You need to enable users to drill down from regional sales summaries to individual store sales and then to customer-level details within the dashboard. Which of the following Snowflake features and dashboard design principles are CRUCIAL for achieving this interactive drill-down capability with optimal performance?

A. Creating multiple dashboards, one for each level of granularity (region, store, customer), and linking them together with navigation buttons.
B. Creating a stored procedure in Snowflake that dynamically generates SQL queries based on user interactions within the dashboard.
C. Relying solely on the dashboard's built-in filtering capabilities and avoiding any pre-aggregation or optimization in Snowflake.
D. Using parameterized views in Snowflake and configuring the dashboard to pass parameters dynamically based on user selections. Ensuring proper clustering keys are defined on relevant tables.
E. Exporting the data to an external BI tool and leveraging its drill-down features. Data can be exported to the external tool daily.

Answer: D

Explanation:
Parameterized views allow you to create flexible queries that adapt to user selections. Clustering keys ensure efficient filtering and data retrieval for drill-down operations. Creating multiple dashboards (B) is less efficient and user-friendly. Relying solely on dashboard filtering (C) can lead to performance issues. Exporting data to an external BI tool (D) introduces latency. Dynamic SQL generation (E) can be complex and prone to errors.

NEW QUESTION # 76
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