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| Section | Objectives |
|---|---|
| Topic 1: Security, Governance, and Data Sharing | - Data sharing and governance
|
| Topic 2: Data Loading and Unloading | - Data export
|
| Topic 3: Data Transformation and Analysis | - SQL-based transformations
|
| Topic 4: Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Topic 5: Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
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問題 #35
Given the following data:
This SELECT statement is executed:
What will be the result?
答案：C
解題說明：
To determine the correct result of the query, a Data Analyst must evaluate the nested functions according to standard SQL order of operations while correctly identifying how Snowflake handles NULL values in aggregate calculations.
Step 1: Evaluating AVG(GRADE)
The AVG() function calculates the arithmetic mean of the non-null values in a column. Based on the provided table data, the grades are: 4, 1, 2, 1, and 2. The sixth row contains a NULL grade. In Snowflake, as per ANSI SQL standards, aggregate functions ignore NULL values entirely. They are not treated as zeros, nor do they cause the function to return NULL (unless the entire column is null).
* Sum of non-null values: $4 + 1 + 2 + 1 + 2 = 10$
* Count of non-null values: $5$
* Result: $10 / 5 = 2.0$
Step 2: Evaluating CEIL(..., 2)
The CEIL (or CEILING) function is typically used to return the smallest integer value that is greater than or equal to the input. However, Snowflake's CEIL function also supports an optional second argument for scale (precision).
* The input is the result from the previous step: 2.0.
* The scale argument is 2.
* The function CEIL(2.0, 2) attempts to round the value up to the nearest value with two decimal places.
Since 2.0 is already a whole number and has no fractional component beyond the specified scale, the ceiling remains 2.0.
Evaluating the Options:
* Option A is incorrect as it represents a massive scaling error.
* Option C and D are incorrect because they result from mathematical errors or incorrectly including the NULL row in the denominator (e.g., $10 / 6 = 1.66$, then rounded up).
* Option B is the 100% correct answer. It accurately reflects the result of the AVG function ($2.0$) followed by the CEIL operation, which preserves the value as it is already at the target ceiling. This question tests precision in both mathematical logic and the technical nuances of Snowflake-specific SQL functions.
問題 #36
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?
答案：B
解題說明：
Materialized views provide precomputed snapshots, enhancing query performance.
問題 #37
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)
答案：A,B
解題說明：
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.
問題 #38
You are using a Snowflake Marketplace data feed that provides daily stock prices. The data is updated daily, and you need to create a process to automatically load the new data into your existing 'STOCK PRICES' table. The Marketplace data feed provides a view called 'MARKETPLACE STOCK PRICES' with columns 'DATE' (DATE), 'SYMBOL' (VARCHAR), and 'PRICE (NUMBER). Your 'STOCK PRICES' table has the same columns. Which of the following Snowflake features or techniques would be BEST suited for automatically loading the new data each day, ensuring that duplicate entries for the same 'DATE and 'SYMBOL' are avoided?
答案：C
解題說明：
Option C is the most appropriate. A 'MERGE statement allows for both inserting new records and updating existing records based on a matching condition (in this case, and 'SYMBOL). This ensures that new data is added, and existing data can be updated if necessary. It also allows for handling potential duplicate scenarios. Option A will simply insert all data, leading to duplicates. Option B is not applicable as Snowpipe ingests from stages not views. Option D is incorrect as Snowpipe is designed for external stages, not internal Marketplace feeds. Option E, while a valid approach for change data capture, is more complex and unnecessary for a simple daily load, also the marketplace feeds don't support change data capture through Streams
問題 #39
How does leveraging partition pruning enhance query performance in Snowflake?
答案：D
解題說明：
Partition pruning optimizes query planning by excluding unnecessary partitions from query execution, improving query performance by focusing on relevant data subsets.
問題 #40
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