質問 1：
A marketing company is analyzing customer purchase data stored in Snowflake to understand which customer demographics are most likely to purchase a newly launched product. The 'CUSTOMERS table has columns: 'customer_id', 'age' , 'gender' , 'location' , and 'household income'. The 'PURCHASES' table has columns: 'customer_id', 'purchase_date', and 'product id'. Which SQL query would most effectively identify the top three age groups with the highest purchase rate for the new product (product_id = 'NEW PRODUCT')?
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：D
解説: (Pass4Test メンバーにのみ表示されます)
質問 2：
You are analyzing the query execution plan of a complex data transformation pipeline in Snowflake. The plan shows a 'Remote Join' operation with high execution time. The two tables involved, 'CUSTOMER and 'ORDERS' , reside in different Snowflake accounts, and the join is performed on the 'CUSTOMER ID' column. Which of the following actions would MOST effectively optimize this query and reduce the 'Remote Join' execution time?
A. Implement data filtering on the 'CUSTOMER table before the 'Remote Join' to reduce the amount of data transferred across accounts. Using temporary table can be used for this task.
B. Ensure both 'CUSTOMER and 'ORDERS tables have the same clustering key, prioritizing 'CUSTOMER IDS.
C. Increase the warehouse size of the account containing the 'ORDERS' table to improve its processing speed.
D. Create a materialized view in the ORDERS account that pre-aggregates the data needed for the join to reduce the data size sent over the network for remote join.
E. Replicate the smaller table (either 'CUSTOMER or 'ORDERS, based on size) to the same Snowflake account as the larger table to eliminate the remote join.
正解：A,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
You are building a sales performance dashboard in Snowflake for a retail company. The data includes sales transactions, product information, and customer demographics. You need to enable users to drill down from regional sales summaries to individual store sales and then to customer-level details within the dashboard. Which of the following Snowflake features and dashboard design principles are CRUCIAL for achieving this interactive drill-down capability with optimal performance?
A. Relying solely on the dashboard's built-in filtering capabilities and avoiding any pre-aggregation or optimization in Snowflake.
B. Creating a stored procedure in Snowflake that dynamically generates SQL queries based on user interactions within the dashboard.
C. Exporting the data to an external BI tool and leveraging its drill-down features. Data can be exported to the external tool daily.
D. Creating multiple dashboards, one for each level of granularity (region, store, customer), and linking them together with navigation buttons.
E. Using parameterized views in Snowflake and configuring the dashboard to pass parameters dynamically based on user selections. Ensuring proper clustering keys are defined on relevant tables.
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 4：
You have a Snowflake environment where different data analysts run a variety of ad-hoc queries against the same set of tables. You've noticed inconsistent query performance, with some queries running quickly and others taking much longer despite having similar logic. To better manage costs and optimize performance, which of the following strategies would be MOST effective in leveraging virtual warehouse caching and resource management in Snowflake? (Select TWO)
A. Disable result caching globally at the account level to ensure that all queries always retrieve the most up-to-date data.
B. Implement a single, large virtual warehouse shared by all data analysts to maximize resource utilization and caching benefits.
C. Use resource monitors to limit the credit usage of individual virtual warehouses or user groups to control costs and prevent runaway queries.
D. Configure the 'AUTO SUSPEND parameter on all virtual warehouses to be a very short duration (e.g., 60 seconds) to minimize costs when the warehouse is idle.
E. Create separate virtual warehouses for different groups of analysts or types of queries to isolate workloads and prevent resource contention.
正解：C,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
You're tasked with building a data model in Snowflake for a retail company. The company has data on products ('PRODUCTS), sales transactions ('SALES), and customer demographics ('CUSTOMERS). You need to design a star schema to support efficient analysis of sales performance by product category and customer segment. Which of the following statements accurately describes the recommended table design and relationships within the star schema for this scenario?
A. Design a fact table CSALES_FACT) with foreign keys to dimension tables for 'PRODUCTS', 'CUSTOMERS, and a DATE' dimension. The 'SALES_FACT table should contain measures such as 'SALES AMOUNT and 'QUANTITY SOLD.
B. create separate fact tables for each product category (e.g., and link them to the 'CUSTOMERS' dimension table.
C. Create a single, denormalized table containing all product, sales, and customer information to maximize query performance.
D. Maintain the original normalized tables ('PRODUCTS', 'SALES', 'CUSTOMERS') and create views that join these tables as needed for reporting purposes.
E. Use a snowflake schema design, where the dimension tables (e.g., 'PRODUCTS', 'CUSTOMERS) are further normalized into related tables to reduce data redundancy.
正解：A,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 6：
You are tasked with creating a dashboard in Snowsight to visualize sales data'. You have a table 'SALES DATA' with columns 'ORDER_DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'SALES_AMOUNT (NUMBER), and 'REGION' (VARCHAR). The business requirements include the following: 1. Display total sales amount by product category in a pie chart. 2. Display a table showing sales amount for each region for a user-selected date range. 3. Allow the user to filter both visualizations by a specific region.
Which of the following approaches would BEST satisfy these requirements using Snowsight dashboards and features?
A. Create a single Snowsight dashboard with two charts: a pie chart showing total sales by product category using the query 'SELECT PRODUCT_CATEGORY, SUM(SALES AMOUNT) FROM SALES DATA WHERE REGION = $REGION GROUP BY PRODUCT_CATEGORY, and a table showing regional sales using the query 'SELECT REGION, FROM SALES_DATA WHERE ORDER_DATE BETWEEN $START_DATE AND $END_DATE AND REGION = $REGION GROUP BY REGION'. Define three dashboard variables: 'REGION' (Dropdown), 'START DATE (Date), and 'END DATE (Date).
B. Create a single Snowsight dashboard with a Python chart for product category sales, querying data using Snowflake Connector, and a table showing regional sales using SQL query. No dashboard variables are needed, as the Python script handles all filtering.
C. Create two separate dashboards: one for the pie chart and another for the table. Use a global session variable to store the selected region and date range, and access it in the SQL queries for both dashboards.
D. Create two separate charts: a pie chart for product category sales and a table for regional sales. Use the same filter on the dashboard for region, and manually enter the date range in the SQL query for the table chart.
E. Create a view with all calculations of the total sale amount, grouping by product category and region. Then create the dashboard with charts based off of this view. This will allow for easier modification if the business requirements change.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
A marketing company is analyzing customer purchase data stored in Snowflake to understand which customer demographics are most likely to purchase a newly launched product. The 'CUSTOMERS table has columns: 'customer_id', 'age' , 'gender' , 'location' , and 'household income'. The 'PURCHASES' table has columns: 'customer_id', 'purchase_date', and 'product id'. Which SQL query would most effectively identify the top three age groups with the highest purchase rate for the new product (product_id = 'NEW PRODUCT')?
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：D
解説: (Pass4Test メンバーにのみ表示されます)
質問 2：
You are analyzing the query execution plan of a complex data transformation pipeline in Snowflake. The plan shows a 'Remote Join' operation with high execution time. The two tables involved, 'CUSTOMER and 'ORDERS' , reside in different Snowflake accounts, and the join is performed on the 'CUSTOMER ID' column. Which of the following actions would MOST effectively optimize this query and reduce the 'Remote Join' execution time?
A. Implement data filtering on the 'CUSTOMER table before the 'Remote Join' to reduce the amount of data transferred across accounts. Using temporary table can be used for this task.
B. Ensure both 'CUSTOMER and 'ORDERS tables have the same clustering key, prioritizing 'CUSTOMER IDS.
C. Increase the warehouse size of the account containing the 'ORDERS' table to improve its processing speed.
D. Create a materialized view in the ORDERS account that pre-aggregates the data needed for the join to reduce the data size sent over the network for remote join.
E. Replicate the smaller table (either 'CUSTOMER or 'ORDERS, based on size) to the same Snowflake account as the larger table to eliminate the remote join.
正解：A,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
You are building a sales performance dashboard in Snowflake for a retail company. The data includes sales transactions, product information, and customer demographics. You need to enable users to drill down from regional sales summaries to individual store sales and then to customer-level details within the dashboard. Which of the following Snowflake features and dashboard design principles are CRUCIAL for achieving this interactive drill-down capability with optimal performance?
A. Relying solely on the dashboard's built-in filtering capabilities and avoiding any pre-aggregation or optimization in Snowflake.
B. Creating a stored procedure in Snowflake that dynamically generates SQL queries based on user interactions within the dashboard.
C. Exporting the data to an external BI tool and leveraging its drill-down features. Data can be exported to the external tool daily.
D. Creating multiple dashboards, one for each level of granularity (region, store, customer), and linking them together with navigation buttons.
E. Using parameterized views in Snowflake and configuring the dashboard to pass parameters dynamically based on user selections. Ensuring proper clustering keys are defined on relevant tables.
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 4：
You have a Snowflake environment where different data analysts run a variety of ad-hoc queries against the same set of tables. You've noticed inconsistent query performance, with some queries running quickly and others taking much longer despite having similar logic. To better manage costs and optimize performance, which of the following strategies would be MOST effective in leveraging virtual warehouse caching and resource management in Snowflake? (Select TWO)
A. Disable result caching globally at the account level to ensure that all queries always retrieve the most up-to-date data.
B. Implement a single, large virtual warehouse shared by all data analysts to maximize resource utilization and caching benefits.
C. Use resource monitors to limit the credit usage of individual virtual warehouses or user groups to control costs and prevent runaway queries.
D. Configure the 'AUTO SUSPEND parameter on all virtual warehouses to be a very short duration (e.g., 60 seconds) to minimize costs when the warehouse is idle.
E. Create separate virtual warehouses for different groups of analysts or types of queries to isolate workloads and prevent resource contention.
正解：C,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
You're tasked with building a data model in Snowflake for a retail company. The company has data on products ('PRODUCTS), sales transactions ('SALES), and customer demographics ('CUSTOMERS). You need to design a star schema to support efficient analysis of sales performance by product category and customer segment. Which of the following statements accurately describes the recommended table design and relationships within the star schema for this scenario?
A. Design a fact table CSALES_FACT) with foreign keys to dimension tables for 'PRODUCTS', 'CUSTOMERS, and a DATE' dimension. The 'SALES_FACT table should contain measures such as 'SALES AMOUNT and 'QUANTITY SOLD.
B. create separate fact tables for each product category (e.g., and link them to the 'CUSTOMERS' dimension table.
C. Create a single, denormalized table containing all product, sales, and customer information to maximize query performance.
D. Maintain the original normalized tables ('PRODUCTS', 'SALES', 'CUSTOMERS') and create views that join these tables as needed for reporting purposes.
E. Use a snowflake schema design, where the dimension tables (e.g., 'PRODUCTS', 'CUSTOMERS) are further normalized into related tables to reduce data redundancy.
正解：A,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 6：
You are tasked with creating a dashboard in Snowsight to visualize sales data'. You have a table 'SALES DATA' with columns 'ORDER_DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'SALES_AMOUNT (NUMBER), and 'REGION' (VARCHAR). The business requirements include the following: 1. Display total sales amount by product category in a pie chart. 2. Display a table showing sales amount for each region for a user-selected date range. 3. Allow the user to filter both visualizations by a specific region.
Which of the following approaches would BEST satisfy these requirements using Snowsight dashboards and features?
A. Create a single Snowsight dashboard with two charts: a pie chart showing total sales by product category using the query 'SELECT PRODUCT_CATEGORY, SUM(SALES AMOUNT) FROM SALES DATA WHERE REGION = $REGION GROUP BY PRODUCT_CATEGORY, and a table showing regional sales using the query 'SELECT REGION, FROM SALES_DATA WHERE ORDER_DATE BETWEEN $START_DATE AND $END_DATE AND REGION = $REGION GROUP BY REGION'. Define three dashboard variables: 'REGION' (Dropdown), 'START DATE (Date), and 'END DATE (Date).
B. Create a single Snowsight dashboard with a Python chart for product category sales, querying data using Snowflake Connector, and a table showing regional sales using SQL query. No dashboard variables are needed, as the Python script handles all filtering.
C. Create two separate dashboards: one for the pie chart and another for the table. Use a global session variable to store the selected region and date range, and access it in the SQL queries for both dashboards.
D. Create two separate charts: a pie chart for product category sales and a table for regional sales. Use the same filter on the dashboard for region, and manually enter the date range in the SQL query for the table chart.
E. Create a view with all calculations of the total sale amount, grouping by product category and region. Then create the dashboard with charts based off of this view. This will allow for easier modification if the business requirements change.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
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