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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q191-Q196):

NEW QUESTION # 191
You are tasked with creating a Snowsight dashboard to monitor the daily sales performance of an e-commerce platform. The sales data is stored in a table named 'SALES_DATR with columns 'SALE_DATE' (DATE), 'PRODUCT_ID' ONT), 'SALES AMOUNT' (FLOAT), and 'CUSTOMER REGION' (VARCHAR). You need to display a trendline chart showing the total sales amount for each day over the last 30 days, and also a table showing the top 5 regions by total sales amount. Which of the following steps are most efficient to achieve this in Snowsight? (Select TWO)

A. Use two separate worksheets in Snowsight. In the first worksheet, create the time series chart. In the second worksheet, create the region-based sales table. Then, embed one worksheet inside the other to create a combined dashboard.
B. Create two separate tiles in the Snowsight dashboard. One tile should use a time series chart visualizing 'SELECT SALE_DATE, SUM(SALES_AMOUNT) FROM SALES_DATA WHERE SALE_DATE DATEADD(day, -30, GROUP BY SALE_DATE ORDER BY SALE_DATE;' and the other tile should use a table visualizing 'SELECT CUSTOMER REGION, SUM(SALES AMOUNT) FROM SALES DATA GROUP BY CUSTOMER REGION ORDER BY DESC LIMIT 5;'
C. Use the 'Notebook' feature in Snowsight to first perform the aggregations required to obtain the data for the visualizations, and then create visualization tiles by referencing the calculated dataframes. No direct SQL is used in the tiles.
D. Create a single tile using a combination chart in Snowsight. Use 'SALE DATE as the x-axis and 'SUM(SALES AMOUNT)' as the y-axis, specifying a trendline. Then, add a second series to display region-based sales in a separate panel below.
E. Create a single tile with two tabs. The first tab displays the trendline chart using a time series and the second tab displays the region-based sales table. Both tabs use the same underlying SQL query but filter the results differently.

Answer: B,C

Explanation:
Option A is correct because it directly and efficiently creates the desired visualizations in Snowsight using separate tiles. Option C is also correct. Using Snowsight's Notebook feature allows for pre-aggregation, which can be useful in complex scenarios where directly visualizing from the raw data would impact dashboard performance. Options B, D, and E are either not directly supported or less efficient methods within the current Snowsight dashboarding capabilities.

NEW QUESTION # 192
You are tasked with cleaning a 'customer_orders' table in Snowflake. The table contains columns like 'order_id', 'customer_id', 'order_date', and 'order_amount'. You notice that some 'order_amount' values are negative (representing returns), but you need to analyze total sales. Additionally, some 'order_date' values are in the future. Which of the following SQL transformations would BEST address these data quality issues to ensure accurate sales analysis?

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

Answer: A

Explanation:
Option C correctly addresses both issues. ensures that all amounts are positive, effectively treating returns as positive contributions to sales for the purpose of this specific analysis. 'order_date <= filters out future dates. Options A and D also exclude negative order amounts which is incorrect, whereas options B and E do not make all amounts positive for sales amount. The use of CTE is also correct. The question is testing the candidate on how to select the best transformations for given data challenges.

NEW QUESTION # 193
You have a Snowflake table named 'CUSTOMER DATA with a 'JOIN DATE column currently stored as VARCHAR. You need to convert this column to a DATE data type. However, the 'JOIN DATE column contains various date formats, including 'YYYY-MM-DD', 'MM/DD/YYYY', and some invalid date strings like 'UNKNOWN'. Which combination of Snowflake SQL functions and techniques provides the MOST robust solution to convert the column to a DATE data type while handling invalid values gracefully?

A. Use 'YYYY-MM-DD')' , then update any resulting NULL values with a default date like '1900-01-01
B. Use 'YYYY-MM-DD')' along with error handling using 'BEGIN...EXCEPTION...END block to capture conversion errors and update the corresponding rows to a default date.
C. Use 'TO DATE(JOIN DATE)' and handle errors during the conversion process by correcting invalid date formats manually.
D. Create multiple temporary tables for each date format using 'TO DATE, then union them together, handling the conversion errors through separate logic.
E. Use 'CASE statements with 'TRY TO DATE and format strings to handle multiple date formats and assign a default value to rows that cannot be converted to a date.

Answer: E

Explanation:
Option C offers the most robust solution. Using 'CASE' statements with 'TRY TO DATE' allows you to specify multiple format strings to handle the different date formats present in the 'JOIN_DATE column. The function gracefully handles invalid date strings by returning NULL, which can then be replaced with a default value using the 'ELSE clause in the 'CASE' statement. This approach avoids errors during conversion and ensures that all rows have a valid date value or a meaningful default.

NEW QUESTION # 194
You are analyzing website traffic data in Snowflake to identify potential bot activity. You have a table 'WEB EVENTS' with columns 'event_timestamp' (TIMESTAMP NTZ), 'user_id' (VARCHAR), and 'ip_address' (VARCHAR). Which combination of SQL techniques and Snowflake features would be MOST effective in detecting and flagging suspicious bot-like behavior, considering high query performance and scalability?

A. Join the table with a publicly available list of known bot IP addresses. Flag any events originating from those IP addresses as potential bot activity. Supplement this with simple frequency counts of events per user.
B. Implement a stored procedure that iterates through each unique IP address in the table, calculating the average time between events for each 12 Flag IP addresses where the average time between events is significantly below a pre-defined threshold.
C. Use a UDF (User-Defined Function) written in Python to perform complex behavioral analysis on user event sequences, checking for patterns like rapid page transitions or form submissions within unrealistic timeframes. Apply this UDF to the 'WEB EVENTS' table.
D. Calculate event frequency per user and IP address using window functions (e.g., 'COUNT() OVER (PARTITION BY user_id, ip_address ORDER BY Then, identify users/lPs with abnormally high event rates within short time intervals using appropriate threshold criteria.
E. Create a scheduled task that periodically runs a query to analyze the ratio of human-generated events to server-generated events. If the ratio drops below a certain threshold, flag the time period as suspicious.

Answer: A,D

Explanation:
Options B and C offer a good balance of effectiveness and efficiency. Option B uses window functions, a powerful feature within Snowflake for analyzing data within a context (user and IP address). Option C uses a pre-defined list of bots and it is not resource intensive. Option A, while potentially accurate, can be computationally expensive due to the use of a UDF and might affect the overall cluster performance. Option D is better suited to detect DDoS attacks. Option E is inefficient as it iterates through the resultset

NEW QUESTION # 195
You need to create an exact, point-in-time copy of a production database named 'CUSTOMER DATA for testing purposes, without impacting the performance of the production system. You want to ensure that the test database 'CUSTOMER DATA TEST uses minimal storage. Which of the following Snowflake commands provides the MOST efficient way to achieve this?

Answer: B

Explanation:
The 'CREATE DATABASE CUSTOMER DATA TEST CLONE CUSTOMER DATA;' command is the MOST efficient because it leverages Snowflake's zero-copy cloning feature. Cloning creates a metadata copy of the database; data is only physically copied when changes are made to the clone. This minimizes storage usage and has a minimal performance impact on the production database. Option A is used for replication across accounts/regions. Option B is not a valid Snowflake command. Option D only copies the structure, and not the data. Option E copies the data into a table within a new or existing database, not cloning the entire database structure.

NEW QUESTION # 196
......

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