Free Access Snowflake.DAA-C01.v2025-12-20.q172 Practice Test (Page 21)

Question 96

You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?

A.Option A

B.Option B

C.Option C

D.Option D

E.Option E

Question 97

When designing a data collection system, what factors should be considered when assessing how often data needs to be collected? (Select all that apply)

A.Volume of data

B.Business requirements

C.Data collection tool limitations

D.Data source availability

Question 98

A financial institution stores transaction data with various attributes, including transaction ID, account ID, transaction type, amount, and timestamp. They need to build a dashboard to monitor transaction volumes and identify unusual patterns in real-time. The data is streaming continuously. Consider these requirements: near real-time analysis, complex pattern recognition, and the need to store the raw transaction data for auditing. Which of the following approaches is optimal, considering the need for both speed and historical data integrity?

A.Create a highly flattened table and use Snowflake's materialized views to aggregate data in near real-time. Store raw data in the same flattened table.

B.Use Snowflake Streams and Tasks to incrementally update a star schema data model in near real-time, with separate tables for transactions, accounts, and transaction types. Store raw data in a separate, immutable table.

C.Directly query the raw streaming data using Snowflake's external tables, without any data transformation or modeling. Rely on Snowflake's query optimization for real-time analysis.

D.Create a single denormalized table, indexed on transaction ID and timestamp. Use this table for both real-time analysis and historical storage.

E.Use Snowflake's Data Marketplace to access pre-aggregated financial data, avoiding the need for any data modeling or transformation.

Question 99

You are analyzing a query profile and observe a high percentage of time spent in 'Remote Scan'. Which of the following actions would be MOST effective in reducing this bottleneck and improving query performance?

A.Increase the warehouse size to allow more concurrent operations.

B.Implement clustering on the table based on the filter criteria used in the query.

C.Convert the table to a transient table to reduce metadata overhead.

D.Create a materialized view that pre-computes the results of the query.

E.Add a 'LIMIT' clause to the query to reduce the amount of data scanned.

Question 100

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.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.

C.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.

D.Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.

E.Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.

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Question 96

Question 97

Question 98

Question 99

Question 100

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