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| Section | Objectives |
|---|---|
| Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
| Data Transformation and Analysis | - SQL-based transformations
|
| Data Loading and Unloading | - Data ingestion methods
|
| Security, Governance, and Data Sharing | - Data sharing and governance
|
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NEW QUESTION # 25
What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)
Answer: A,B,C
Explanation:
General DML operations in Snowflake include inserting, updating, and deleting data.
NEW QUESTION # 26
A data analyst is tasked with creating a near-real-time dashboard using Streamlit and Snowflake to monitor website traffic. The website traffic data is continuously ingested into a Snowflake table named 'WEB TRAFFIC EVENTS' with columns 'EVENT TIME' (TIMESTAMP LTZ), 'PAGE URL' (VARCHAR), and 'USER ID' (VARCHAR). The analyst wants to ensure the Streamlit dashboard automatically reflects the latest data in Snowflake with minimal latency. Which of the following approaches would provide the MOST efficient and near-real-time data updates in the Streamlit dashboard?
Answer: A
Explanation:
Leveraging Snowflake's CDC capabilities is the most efficient way to achieve near-real-time data updates in Streamlit. CDC allows you to retrieve only the changes made to the table since the last refresh, significantly reducing the amount of data transferred and processed compared to querying the entire table each time. Snowpipe is a good option, but is more oriented to data ingestion and not data changes. Option A caches results, which is opposite of real time. Option B is useful, but more complex than needed. Option C is not related to the problem. Option D is incorrect because 'auto-commit' applies only to data updates from the application to Snowflake. Streamlit can then query this CDC stream to get an updated set of data. Using 'st.cache_data' with a short TTL ensures the dashboard reflects recent data changes from the CDC Stream.
NEW QUESTION # 27
A Data Analyst has a very large table with columns that contain country and city names. Which query will provide a very quick estimate of the total number of different values of these two columns?
Answer: B
Explanation:
When working with very large tables, calculating the exact number of unique combinations of columns (cardinality) using COUNT(DISTINCT ...) is a resource-intensive operation. It requires the Snowflake query engine to keep an exhaustive list of every unique pair encountered in memory, which can lead to high credit consumption and performance bottlenecks.
To provide a "very quick estimate," Snowflake utilizes the HyperLogLog (HLL) algorithm. The function HLL(column1, column2, ...) returns an HLL state (a binary representation) that can be used to estimate the number of distinct values with a high degree of accuracy and minimal computational overhead. This is part of Snowflake's suite of approximate aggregation functions, which are essential for Data Analysis on massive datasets where a 1% margin of error is acceptable in exchange for significantly faster results.
Evaluating the Options:
* Option A is syntactically incorrect; DISTINCT cannot be used in that position with COUNT.
* Option C is a valid query but will be significantly slower and more expensive than an HLL estimate on a "very large table".
* Option D simply counts the total number of non-null rows, which does not represent the "number of different values" (cardinality).
* Option B is the 100% correct answer. It specifically addresses the requirement for a "quick estimate" using the industry-standard probabilistic counting method built into Snowflake.
NEW QUESTION # 28
A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)
Answer: A,B
Explanation:
TO_GEOGRAPHY(@service_area_wkt))' correctly identifies points that fall within the service area polygon. converts the WKT string into a GEOGRAPHY object. 'ST_COVERS(TO_GEOGRAPHY(@service_area_wkt), ADDRESS_POINT)' checks if the polygon covers the point.
NEW QUESTION # 29
How do user-defined functions (UDFs) contribute to data analysis in SQL?
Answer: C
Explanation:
UDFs allow customization and extension of SQL functionalities, enabling specific data operations beyond predefined limits.
NEW QUESTION # 30
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