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| Section | Objectives |
|---|---|
| Security, Governance, and Data Sharing | - Access control and security
|
| Data Transformation and Analysis | - SQL-based transformations
|
| Data Loading and Unloading | - Data ingestion methods
|
| Data Modeling and Performance Optimization | - Performance tuning
|
| Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
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NEW QUESTION # 52
A logistics company needs to determine which warehouses are within a 50km radius of a new distribution center. The warehouse locations are stored in a table 'WAREHOUSES' with columns 'WAREHOUSE ID' ONT), (GEOGRAPHY) and the distribution center's location is stored in a variable of type GEOGRAPHY. Which query will efficiently identify all warehouses within the specified radius, returning the 'WAREHOUSE ID and distance in kilometers?
Answer: C
Explanation:
The correct answer uses 'ST DWITHIN' with the correct parameters and unit. 'ST DWITHIN(LOCATION, @distribution_center, 50000)' correctly filters the warehouses based on the 50km (50000 meters) radius. ST_DISTANCE calculates the distance in meters, which is then converted to kilometers by dividing by 1000. The warehouse location should come first followed by the distribution centre in the DWITHIN' Function.
NEW QUESTION # 53
You are working with a Snowflake table called containing sales transactions. The table includes columns like 'transaction_id' (VARCHAR), 'product_id' (VARCHAR), 'transaction_date' (DATE), (NUMBER(10,2)), and (GEOGRAPHY). You need to perform the following data preparation tasks: 1. Clean the data: Remove transactions with less than or equal to 0. 2. Transform the data: Convert the 'customer_location' from GEOGRAPHY to a WKT string representation. 3. Enrich the data: Calculate the year and month from the 'transaction_date' and add them as new columns. 4. Mask the data: Partially mask the 'transaction_id' column to protect customer data by only displaying the last 4 digits of the ID. Considering performance and best practices, what is the MOST efficient way to achieve these transformations using Snowflake features? You need to achieve this in a single statement.
Answer: D,E
Explanation:
Options A and D are functionally equivalent and correct, they both performs the desired data cleansing, transformation, and enrichment in a single 'CREATE OR REPLACE TABLE' statement. The 'WHERE sales_amount > clause filters out invalid transactions. RIGHT(transaction_id, 4) masks the transaction ID. YEAR() and MONTH() extract the year and month from the date, and ST_ASTEXT() converts the GEOGRAPHY object to a WKT string. Option B uses HASH, but the question asks for partially masking so HASH isn't suitable. Option C uses CASE, that is not required. Option E makes use of SYSTEM$MASK, function which masks the entire column value, that isn't what the questions asks.
NEW QUESTION # 54
What does the "SQL keyword" refer to in the context of adding filters to a worksheet?
Answer: A
Explanation:
In Snowsight (Snowflake's web interface), Data Analysts can create interactive dashboards and worksheets using Filters. When you define a filter (such as a Date Range or a list of Regions), you must assign it a SQL Keyword.
This "SQL Keyword" acts as a variable placeholder within your SQL code. For example, if you create a filter for "Customer Region" and set the SQL keyword to :my_region, you can then write a query like:
SELECT * FROM sales WHERE region = :my_region. When a user interacts with the UI and selects "North America" from the dropdown, Snowsight automatically injects "North America" into every instance where :
my_region appears in the worksheet's SQL.
Evaluating the Options:
* Option A is incorrect because the keyword is a label/variable, not the underlying function code.
* Option C and Option D are incorrect as they confuse the data source of the filter values with the reference name used in the SQL code.
* Option B is the correct answer. The SQL Keyword is specifically the identifier (prefixed with a colon in the code) that allows the analyst to link the UI element (the filter) to the execution logic of the query.
This is a fundamental skill for the Data Presentation and Data Visualization domain, ensuring reports are dynamic and user-friendly.
NEW QUESTION # 55
When choosing between using a dimensional model and a flattened dataset for BI requirements in Snowflake, what considerations impact the final decision? (Select all that apply)
Answer: A,C
Explanation:
Considerations such as query performance expectations and data denormalization needs influence the choice between a dimensional model and a flattened dataset, affecting query optimization and data structure suitability for BI purposes.
NEW QUESTION # 56
A key aspect of performing exploratory ad-hoc analyses is:
Answer: C
NEW QUESTION # 57
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