DAA-C01 Exam Dumps | You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance.

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Question 110/151

You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance. The dashboard requires two key features: 1) users should be able to filter the data by product category dynamically using a dropdown list. 2) The dashboard should efficiently handle large datasets (millions of rows) without performance degradation. Which Snowflake features and techniques would you use to achieve these requirements most effectively?

A. Use Snowflake's dynamic data masking to hide sensitive sales data based on user roles, and create a view with pre-aggregated data for each product category to improve dashboard performance.

B. Implement user-defined functions (IJDFs) in Python to perform custom filtering logic, and store the results in a temporary table for the dashboard to consume.

C. Utilize Snowflake's caching mechanisms and create materialized views to pre-compute aggregated data for the dashboard, along with using dashboard-level filtering widgets connected to the product category column.

D. Create a stream on the sales data and use a task to continuously update a summary table with aggregated data for each product category. Use the summary table as the data source for the dashboard.

E. Extract all data into a pandas DataFrame and create a Dash application for the front end, as Snowflake struggles to handle real-time filtering effectively.

Correct Answer: C

Using materialized views pre-computes aggregates and leverages Snowflake's caching. Dashboard-level filtering (option C) allows for dynamic filtering without querying the base data directly, providing the best performance and scalability. Options A and D are less ideal for real-time filtering; B is an anti-pattern in Snowflake, and E moves data out of Snowflake unnecessarily.

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