DAA-C01 Exam Dumps | A financial institution stores transaction data with various attributes, including transaction ID, account

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Question 99/151

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.

Correct Answer: B

Option B is the most suitable solution. Snowflake Streams and Tasks provide a mechanism for incrementally updating a data model (in this case, a star schema) based on changes in the source data. This enables near real-time updates to aggregated tables used for the dashboard. Storing the raw data in a separate, immutable table ensures data integrity for auditing purposes. Option A might work, but managing large flattened tables with constant updates can be challenging. Option C is not efficient for complex analysis. Option D lacks the structure needed for complex joins and aggregations. Option E is irrelevant as it proposes using external data instead of processing the institution's own data.

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