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Consider the following Snowflake table schema and data: 'CREATE TABLE products (product_id INTEGER, product_name VARCHAR, properties VARIANT);' Data: 'INSERT INTO products VALUES (1, 'Laptop', "silver", "storage": "512GB", "price": 1200.00}'));' 'INSERT INTO products VALUES (2, 'Mouse', "wireless", "dpi": 1600, "price": 25.00}'));' 'INSERT INTO products VALUES (3, 'Keyboard', PARSE JSON('{"layout": "US", "backlit": true, "price": Which of the following SQL queries will return the 'product_name' and 'price' for all products where the 'price' is greater than 50, ensuring that the 'price' is treated as a numeric value for comparison? Select all that apply
A.
B.
C.
D.
E.
Answer: A,C
Explanation:
Options B and E are correct. Option B explicitly casts 'properties:price' to a 'NUMBER data type before the comparison, ensuring that the comparison is performed numerically. Option E casts 'properties:price' to a data type and uses TRY_TO_NUMBER to handle potential errors gracefully. Option A is incorrect because Snowflake treats the value extracted from the VARIANT as a string and the string comparison will lead to incorrect results. Options C and D don't work without casting to VARCHAR
NEW QUESTION # 33
You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as 'CUSTOMER ID, 'NAME', 'EMAIL', 'PHONE NUMBER', and 'ADDRESS'. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent '+1-XXX-XXX-XXXX' format (where X is a digit). 2) Remove any leading or trailing whitespace from the 'NAME and 'ADDRESS' columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?
A.
B.
C.
D.
E.
Answer: E
Explanation:
Option D correctly addresses all requirements by performing all transformations within a single CREATE OR REPLACE TABLE statement. This ensures atomicity and avoids multiple update operations. It correctly trims NAME and ADDRESS, replaces invalid emails with NULL, and formats phone numbers if they have exactly 10 digits after removing non-numeric characters, otherwise assigning NULL. The correct option must format phone number as well as replace with NULL, while not replacing with NULL would mean keep original. Option E keeps original value not NULL, if phone number doesn't match criteria.
NEW QUESTION # 34
In Snowflake, how does partition pruning contribute to optimizing query performance?
A. Increases query complexity and optimization
B. Limits data accessibility across warehouses
C. Filters unnecessary partitions during query execution
D. Impacts query planning but not execution
Answer: C
Explanation:
Partition pruning in Snowflake filters out unnecessary partitions during query execution, significantly enhancing query performance by minimizing the data scanned during the query.
NEW QUESTION # 35
When working with CSV, JSON, and Parquet data types in Snowflake, how does selecting the appropriate data format impact query performance and storage efficiency?
A. JSON format accelerates query execution but increases storage requirements
B. All formats have similar impacts on query performance and storage
C. CSV format enhances query performance and minimizes storage needs
D. Parquet format optimizes query performance and reduces storage requirements
Answer: D
Explanation:
Parquet format in Snowflake optimizes query performance and storage efficiency by enhancing data compression, reducing storage needs, and accelerating query execution compared to other formats like CSV and JSON.
NEW QUESTION # 36
You are designing a dimensional model for a subscription-based service. You have a 'FACT SUBSCRIPTIONS' table with columns like 'subscription_id', 'customer id', 'start date', 'end date', and 'subscription_amount'. The business wants to analyze monthly recurring revenue (MRR) and churn rate. You need to model the temporal aspect of subscriptions to accurately calculate these metrics. Select the TWO best approaches to model the time dimension to facilitate these calculations:
A. Create a 'DIM MONTH' table with columns like 'month id', 'month start date', and 'month end date' and link the 'FACT SUBSCRIPTIONS table to it based on the 'start_date' falling within the month.
B. Create a 'DIM_DATE table and link 'FACT_SUBSCRIPTIONS' to it using 'start_date' and 'end_date' columns.
C. Create a 'DIM_SUBSCRIPTIOW table with 'subscription_id' as the primary key and store all subscription details there, avoiding the need for a fact table.
D. Create a snapshot fact table FACT SUBSCRIPTION SNAPSHOTS that captures the state of each subscription at the end of each month. This table would include 'subscription_id' , 'customer_id', 'snapshot_date', 'is_active' , and 'subscription_amount' .
E. Store 'start_date' and 'end_date' as VARCHAR columns in the 'FACT SUBSCRIPTIONS table to avoid data type conversions.
Answer: A,D
Explanation:
Creating a snapshot fact table (option B) allows for direct calculation of MRR and churn at a specific point in time. Analyzing subscription state at monthly intervals is helpful for these metrics. Creating a 'DIM_MONTH' table (option E) simplifies grouping and aggregation of subscriptions by month. Option A (linking to 'DIM DATE' using both start and end dates) might be useful for other types of analysis, but not directly for MRR/churn calculations. Storing dates as VARCHAR (option C) is bad practice and will hinder performance. A dimension table for subscriptions (option D) won't capture the temporal changes necessary for these metrics.
NEW QUESTION # 37
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