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How do materialized views differ from regular views in terms of data access and storage?
A. Materialized views restrict data access for improved security.
B. Regular views enhance data accessibility more effectively than materialized views.
C. Materialized views provide precomputed snapshots, unlike regular views.
D. Regular views offer better storage optimization compared to materialized views.
正解：C
解説：
Materialized views provide precomputed snapshots, differentiating them from regular views.
質問 # 48
A retail company is analyzing sales data to optimize product placement and promotional campaigns. They have sales figures, customer demographics, and promotional campaign details stored in Snowflake. Which visualization technique and Snowflake feature combination would BEST help them identify nuanced correlations between customer age, product category, and the success rate of different promotional campaigns, allowing for interactive exploration and drill-down capabilities?
A. Export the data to a local CSV file and use Python's Pandas library to generate scatter plots showing the relationship between age, product category, and promotional campaign success. Create simple Histogram with no drilldown capabilities
B. Utilize Snowflake's Data Marketplace to access pre-built dashboards focusing on retail analytics, adapting them to visualize the company's data without further customization.
C. Develop an interactive dashboard using Streamlit connected to Snowflake, implementing cross-filtering between visualizations of customer age distribution, product category sales, and promotional campaign ROI. Use stored procedures in Snowflake to pre-calculate aggregated data and improve dashboard performance. Utilize heatmap to find correlation between Customer Age and Sales for promotion campaigns
D. Create a static bar chart in Tableau showing average sales per age group for each product category. Use Snowflake's aggregate functions to calculate averages.
E. Use Snowflake's built-in SQL to generate a correlation matrix between customer age and sales, and present it as a table in a static report. Implement UDF to calculate more customized Correlation coefficeint like Kendall+s Tau if data has non-normal distribution.
正解：C
解説：
Option B is the most appropriate because it leverages an interactive dashboard (Streamlit) connected to Snowflake, enabling cross- filtering and drill-down capabilities. Using stored procedures in Snowflake to pre-calculate aggregated data enhances dashboard performance. Option A provides a static view, lacking interactivity. Option C relies on a static table, which is not ideal for exploratory analysis. Option D involves exporting data outside Snowflake, which is inefficient and less secure. Option E might not provide the specific visualizations or level of customization needed for detailed correlation analysis. The use of the heatmap allows a clearer correlation view.
質問 # 49
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.
A. Option C
B. Option B
C. Option E
D. Option D
E. Option A
正解：A
解説：
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.
質問 # 50
A financial institution uses Snowflake to store customer transaction data'. They need to create a dashboard that visualizes daily transaction volume and average transaction amount for fraud detection purposes. This dashboard needs to be automatically updated every hour. The current dashboard query performance is slow, especially during peak hours. Given that the 'TRANSACTIONS table contains billions of rows, which of the following strategies would BEST optimize both the query performance and the automated update process?
A. Implement caching within the dashboard application to store the query results and only refresh the data once a day to avoid performance issues
B. Create a materialized view that pre-aggregates the daily transaction volume and average transaction amount. Schedule a Snowflake task to refresh the materialized view hourly.
C. Increase the warehouse size to X-Large and rely on Snowflake's query optimization engine without any changes to the data model or update process.
D. Create a regular view that calculates daily transaction volume and average transaction amount. Use a Snowflake stored procedure to execute the query and update a separate reporting table hourly.
E. Create a temporary table that stores daily transaction summaries. Truncate and reload the temporary table hourly using a scheduled Snowflake task.
正解：B
解説：
Materialized views are designed for pre-computation of aggregations, providing significant performance improvements. Scheduling a task to refresh the materialized view ensures automatic updates. Regular views are calculated at query time and would not improve performance. Increasing warehouse size (C) might help, but it's not the most efficient solution. Temporary tables are not persistent and truncation/reload is inefficient. Dashboard caching (E) does not solve underlying Snowflake performance issues.
質問 # 51
How does automating and implementing data processing contribute to the overall efficiency of data ingestion?
A. Implementing data processing increases data redundancy.
B. Automation eliminates human intervention, ensuring consistency.
C. It slows down the data ingestion process significantly.
D. Automating processing hampers data accuracy.
正解：B
解説：
Automation ensures consistency and eliminates manual interventions, enhancing the efficiency of data ingestion.
