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| Section | Objectives |
|---|---|
| Topic 1: Data Transformation and Analysis | - Analytical workloads
|
| Topic 2: Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Topic 3: Data Modeling and Performance Optimization | - Performance tuning
|
| Topic 4: Data Loading and Unloading | - Data export
|
| Topic 5: Security, Governance, and Data Sharing | - Data sharing and governance
|
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NEW QUESTION # 29
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: B
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 # 30
You are tasked with cleaning a dataset containing customer information. One of the columns, 'PHONE NUMBER', contains inconsistent data: some entries have leading country codes (+1), some have dashes, and others have no separators at all. You need to create a function that standardizes all phone numbers to a consistent format (e.g., '1234567890'). Which of the following Snowflake UDFs (User- Defined Functions) effectively cleans and standardizes the phone numbers, removing all non-numeric characters and country codes?
Answer: A,B
Explanation:
Option A and E are correct. Both use REGEXP REPLACE to remove any non-numeric characters (to-9]+). Then, they use REGEXP_REPLACE again to remove the leading country code (+1 or 1). The other options are either too simplistic (only handling specific characters) or don't fully remove the country code reliably. Option A removes leading '1+' and Option E removes '+1'. The '+ sign needs to be escaped in the regex pattern.
NEW QUESTION # 31
You are responsible for maintaining a dashboard that visualizes sales data'. The dashboard relies on a Snowflake view named 'SALES SUMMARY. Users have reported slow refresh times, especially when filtering by date. The 'SALES SUMMARY view is defined as follows:
Which of the following actions could significantly improve the dashboard's performance when filtering by date, assuming 'SALES_TRANSACTIONS' table is large?
Answer: D
Explanation:
Clustering the 'SALES_TRANSACTIONS table by the DATE column is the most effective solution. Snowflake uses micro- partitioning, and clustering helps to logically group similar data together within micro-partitions. When filtering by DATE, Snowflake can then efficiently prune micro-partitions that do not contain relevant data, significantly reducing the amount of data scanned. Materialized views (A) can help but need to be maintained and add complexity. Search optimization on DATE can help but it's not the intended use case, as DATE is a range based column (B). Ordering the results (C) doesn't affect query performance significantly. Partitioning is not directly supported in Snowflake. Clustering is the correct approach.
NEW QUESTION # 32
What does the "SQL keyword" refer to in the context of adding filters to a worksheet?
Answer: D
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 # 33
You have been tasked with creating a reporting solution for a marketing campaign. The source data includes customer information (name, email, location), campaign details (campaign ID, name, start date, end date), and interaction data (customer ID, campaign ID, interaction type, timestamp). The business users want to analyze campaign performance based on customer segments, interaction types, and time periods. The data volume is expected to be moderately large (millions of records), and the reporting requirements are relatively stable. Which of the following data modeling and transformation strategies would be most efficient and maintainable?
Answer: C
Explanation:
Option B offers the most efficient and maintainable solution. A star schema provides a good balance between query performance and data maintainability. The fact table stores the interactions, while the dimension tables provide context for customers, campaigns, and time. Snowflake's clustering feature can be used to physically organize the data on disk based on common query patterns, further improving performance. A fully denormalized table (Option A) can lead to data redundancy and update anomalies. Option C makes reporting complex and inefficient. Option D adds complexity without significant performance benefits in this scenario. Option E is inefficient for this use case. Given that we expect stable reporting requirements and moderately large data, it is best to flatten the data using a well-defined data model.
NEW QUESTION # 34
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