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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q266-Q271):

NEW QUESTION # 266
You have a large table 'WEB EVENTS with columns 'EVENT TIMESTAMP, 'USER ID', 'PAGE URL', and 'EVENT _ TYPE. You need to create a materialized view that efficiently calculates the daily unique user count for a specific set of 'PAGE URL' values. The 'WEB EVENTS table is frequently updated. Which of the following approaches would be MOST performant and scalable for this scenario?

A. Ingest the daily unique user count data via a 3rd party tool into a new table and create a view using that table.
B. Create a materialized view using a window function to calculate the running total of unique users each day, then extract the final value for each day.
C. Create a standard view that filters the 'WEB_EVENTS' table and calculates 'COUNT(DISTINCT USER_ID)' grouped by and 'PAGE URL'.
D. Create a materialized view that directly selects 'COUNT(DISTINCT USER_ID)' grouped by ' and with filtering on the desired 'PAGE URL' values.
E. Create a materialized view that first calculates the total number of events for each user on each day and then aggregates that data to calculate the unique user count.

Answer: D

Explanation:
Calculating 'COUNT(DISTINCT directly in the materialized view is the most efficient approach. Pre-calculating unnecessary aggregates adds overhead. Standard views do not provide the performance benefits of materialized views. Window functions are generally less performant than direct aggregations in this scenario, and the new data can be calculated directly in Snowflake, avoiding dependency with 3rd party tools.

NEW QUESTION # 267
You are tasked with creating a dashboard to monitor the performance of different marketing channels (e.g., email, social media, paid advertising). The data includes daily spend, impressions, clicks, and conversions for each channel. Which approach would BEST allow you to visualize the return on investment (ROI) for each channel over time, identify channels with diminishing returns, and enable stakeholders to easily compare channel performance?

A. Create a static report in Tableau using only aggregate measures to calculate the total ROI for each channel and display it in a table.
B. Create separate line charts for each channel showing spend, impressions, clicks, and conversions over time, using a static reporting tool like SSRS.
C. Develop an interactive dashboard in Looker Studio, utilizing calculated fields to derive ROI for each channel (e.g., conversions / spend). Use a combination of line charts, bar charts (ROI per channel), and scatter plots (spend vs. conversions) with trendlines. Implement drill-down capabilities to view daily performance metrics.
D. Use Snowflake's built-in charting capabilities to create a series of pie charts showing the percentage of total spend allocated to each channel.
E. Export the data to Excel and create a pivot table summarizing spend and conversions for each channel. Generate a simple bar chart showing total ROI for each channel.

Answer: C

Explanation:
Option B is the most suitable because it uses an interactive dashboard (Looker Studio) with calculated fields to derive ROI. The combination of line charts, bar charts, and scatter plots provides a comprehensive view of channel performance over time. Trendlines in the scatter plots help identify diminishing returns. Drill-down capabilities allow for detailed analysis. Option A creates separate charts, making comparison difficult. Option C is limited to summary data. Option D focuses on spend allocation, not ROI. Option E provides only a static view of total ROI.

NEW QUESTION # 268
You are tasked with cleaning a 'COMMENTS table that contains user-generated comments in a column (VARCHAR). The comments often contain HTML tags, excessive whitespace, and potentially malicious scripts. Your goal is to remove all HTML tags, trim leading and trailing whitespace, and escape any remaining HTML entities to prevent script injection vulnerabilities. Which combination of Snowflake scalar functions provides the most robust and secure way to achieve this data cleaning?

A. SELECT >', FROM COMMENTS WHERE
B. SELECT >', FROM COMMENTS;
C. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
D. SELECT TRIM(REGEXP >', FROM COMMENTS;
E. SELECT >', comment_text) FROM COMMENTS;

Answer: B

Explanation:
Option B is the most robust and secure method. Here's why: 'REGEXP REPLACE(comment_text, Y', "Y: This removes HTML tags. This attempts to parse the remaining text as XML. If there are still any unescaped or malformed HTML entities, this step will help to isolate them and get rid of the tags. If the text cannot be parsed as XML, PARSE_XML returns NULL. '$').$: This extracts the text content of the XML. Crucially, 'XMLGET' inherently performs HTML entity decoding, effectively escaping potentially dangerous characters (e.g., becomes This prevents script injection. This removes leading and trailing whitespace. Option A only removes the HTML tags and trims the text, but doesn't handle HTML entity encoding, and thus it is vulnerable to script injection. Option C is not correct as HTML ENTITY DECODE' is not an existing function in Snowflake. Option D is not correct as the text needs to be cleaned irrespective of whether it contains XML or not. Option E - if parsing the XML returns null then original value gets returned , which we don't want , we would need to make the value NULL.

NEW QUESTION # 269
A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)

Answer: C,D

Explanation:
TO_GEOGRAPHY(@service_area_wkt))' correctly identifies points that fall within the service area polygon. converts the WKT string into a GEOGRAPHY object. 'ST_COVERS(TO_GEOGRAPHY(@service_area_wkt), ADDRESS_POINT)' checks if the polygon covers the point.

NEW QUESTION # 270
You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as 'CUSTOMER ID, 'NAME', 'EMAIL', 'PHONE NUMBER', and 'ADDRESS'. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent '+1-XXX-XXX-XXXX' format (where X is a digit). 2) Remove any leading or trailing whitespace from the 'NAME and 'ADDRESS' columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?

Answer: E

Explanation:
Option D correctly addresses all requirements by performing all transformations within a single CREATE OR REPLACE TABLE statement. This ensures atomicity and avoids multiple update operations. It correctly trims NAME and ADDRESS, replaces invalid emails with NULL, and formats phone numbers if they have exactly 10 digits after removing non-numeric characters, otherwise assigning NULL. The correct option must format phone number as well as replace with NULL, while not replacing with NULL would mean keep original. Option E keeps original value not NULL, if phone number doesn't match criteria.

NEW QUESTION # 271
......

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