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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q148-Q153):

NEW QUESTION # 148
An organization needs to provide daily summaries of key metrics to its executive team via email. They currently have a complex SQL query that generates the report, but the query takes several minutes to run. The executive team needs the data before 8 AM daily. The data warehouse is also used for other reporting and analytics tasks throughout the day. Given these constraints, what is the MOST efficient and reliable approach to operationalize this report?

A. Schedule a Snowflake task to execute the complex SQL query directly and email the results using Snowflake's email integration. Ensure the task runs on a large warehouse.
B. Use Snowflake's Data Marketplace to find a pre-built reporting solution that matches their requirements.
C. Create a Snowpipe to continuously ingest data into a separate table. Write a simplified SQL query on the target table to generate the report. Schedule a task to execute the query and email the results.
D. Use a third-party ETL tool to extract the data, transform it, and generate the report. Schedule the ETL job to run nightly and email the report.
E. Create a materialized view based on the complex SQL query. Schedule a task to refresh the materialized view nightly before 8 AM. Create a separate, simpler query against the materialized view to generate the report and email it.

Answer: E

Explanation:
Creating a materialized view (B) is the most efficient approach. Materialized views pre-compute the results, so the nightly task refreshes the pre-calculated data. This ensures fast report generation before 8 AM, even with a complex underlying query. Running the complex query directly (A) might exceed the time limit. Using a third-party ETL tool (C) adds unnecessary complexity. Snowpipe (D) is for continuous data ingestion, not report generation. Data Marketplace (E) is unlikely to perfectly match the custom needs.

NEW QUESTION # 149
A data analyst accidentally dropped a crucial table, 'SALES DATA', containing historical sales information. The table was dropped 5 days ago. The data retention period for the Snowflake account is set to the default value. The analyst needs to recover the table with all its data'. What is the MOST efficient and reliable method to recover the 'SALES DATA" table in Snowflake?

A. Request Snowflake Support to restore the table from their backups.
B. create a clone of the table using Time Travel at a point in time before it was dropped, using the 'CREATE TABLE CLONE SALES DATAAT (OFFSET -86400 5)' command.
C. Use the 'TIME TRAVEL' function in a SELECT statement to retrieve the data and recreate the table.
D. Restore the table using the 'UNDROP TABLE SALES DATA' command.
E. create a clone of the table using Time Travel at a point in time before it was dropped, using the 'CREATE TABLE CLONE SALES DATA BEFORE(STATEMENT y command.

Answer: D

Explanation:
The 'UNDROP TABLE command is the most efficient and direct method to recover a dropped table, as long as it's within the data retention period. Since the table was dropped 5 days ago and the default retention period is typically sufficient (can be up to 90 days in Enterprise Edition), 'UNDROP TABLE should work. While options B and E are valid uses of cloning and time travel, they involve creating a new table and are less direct. Requesting Snowflake support (C) is unnecessary for a simple table recovery. Option D will require data extraction and recreating the table structure, which is tedious and time-consuming compared to 'UNDROP'

NEW QUESTION # 150
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

A. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.
B. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
C. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
D. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.
E. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.

Answer: A,B

Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.

NEW QUESTION # 151
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?

Answer: C

Explanation:
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.

NEW QUESTION # 152
You are working with a 'product catalog' table that contains 'product id', 'product_name', 'category', and 'price'. You notice that some product names have leading and trailing whitespace, inconsistent capitalization, and special characters. Which combination of Snowflake functions and techniques would provide the MOST comprehensive solution for cleaning the 'product_name' column to ensure consistency and accuracy in your analysis? (Select all that apply)

A. Use ' REPLACE(Y in a nested fashion, or regular expression functions like 'REGEXP REPLACE()' , to remove or replace special characters.
B. Use or to standardize capitalization.
C. Manually review and update each product name individually within the table using UPDATE statements.
D. Use 'TRIM()' to remove leading and trailing whitespace.
E. Create a Snowflake UDF (User-Defined Function) that encapsulates all the cleaning steps for reuse and better code organization.

Answer: A,B,D,E

Explanation:
Options A, B, C, and D are all valid and contribute to a comprehensive solution. 'TRIM()' removes whitespace, 'UPPER()' or 'LOWER()' standardizes capitalization, 'REPLACE()' or 'REGEXP handles special characters, and a UDF allows for reusable and organized code. Option E is highly inefficient and not scalable for large datasets. The question targets knowledge of various Snowflake cleaning functions and UDF for reusability.

NEW QUESTION # 153
......

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