Associate-Data-Practitioner Exam Dumps | Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays

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Question 39/47

Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.

D. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Correct Answer: C

Creating asingle Explorewith all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.
Looker's recommended approach for dashboards is a single, unified Explore for scalability and usability, supporting filters and drill-downs.
* Option A: Materialized views in BigQuery optimize queries but bypass Looker's modeling layer, reducing flexibility.
* Option B: Custom visualizations are for specific rendering, not multi-metric dashboards with filtering
/drill-down.
* Option C: Multiple Explores fragment the data model, complicating dashboard cohesion and maintenance.

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