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| Section | Objectives |
|---|---|
| Data Loading and Unloading | - Data ingestion methods
|
| Data Transformation and Analysis | - SQL-based transformations
|
| Data Modeling and Performance Optimization | - Performance tuning
|
| Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Security, Governance, and Data Sharing | - Access control and security
|
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NEW QUESTION # 25
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?
Answer: A
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 # 26
When maintaining reports and dashboards, why is it essential to build automated and repeatable tasks?
Answer: C
Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.
NEW QUESTION # 27
You are tasked with preparing a large dataset of website clickstream data stored in a Snowflake table named This table contains a 'timestamp' column (TIMESTAMP NTZ), a column (VARCHAR), and a 'page_urr column (VARCHAR). You need to identify the most popular pages visited by users during specific hours of the day, but only for users who have visited at least 5 unique pages in the dataset. Which sequence of SQL operations, potentially including temporary tables or CTEs, would efficiently achieve this goal in Snowflake?
Answer: A,C
Explanation:
Option B and E are the most efficient solutions. Option B uses a CTE (Common Table Expression) to filter users who have visited at least 5 unique pages and then joins this result with the original table to calculate visit counts by hour and page. Option E creates a TEMP table for qualified users then joins against it to create a final aggregated set. A is functionally correct but may not be as optimized as using a CTE and uses two queries. C has an unnecessary subquery in the WHERE clause, which can degrade performance. D uses inline view which is less readable and less efficient than CTE. Both CTEs (B) and temporary tables (E) are common techniques in Snowflake for breaking down complex queries and can improve readability and maintainability. Snowflake's query optimizer is generally very good, and the CTE approach (B) is often preferred for its clarity.
NEW QUESTION # 28
When planning for data volume collection, what is an important consideration to ensure scalability and performance?
Answer: D
NEW QUESTION # 29
Your organization is migrating its data warehouse to Snowflake. You need to monitor the resource consumption of different users. You want to identify which users are running the most expensive queries (in terms of credits consumed) over the last 7 days. You need to create a query using system functions to achieve this. Which of the following queries will accurately provide this information?
Answer: A
Explanation:
The correct query should use the 'snowflake.account_usage.query_history' view and specifically sum the column as this reflects the credits used for the compute resources by query. Option C accurately reflects this. Option A is incorrect because the 'credits_used' column in 'query_history' does not give a direct credit consumption cost based on computation. Option B uses 'execution_history' which does not aggregate the same detailed credit usage information. Option D sums credits used for cloud services not the compute. Option E references warehouse metering history, not specific users' query execution history.
NEW QUESTION # 30
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