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| Section | Objectives |
|---|---|
| Topic 1: Data Transformation and Analysis | - SQL-based transformations
|
| Topic 2: Security, Governance, and Data Sharing | - Data sharing and governance
|
| Topic 3: Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Topic 4: Data Modeling and Performance Optimization | - Performance tuning
|
| Topic 5: Data Loading and Unloading | - Data export
|
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NEW QUESTION # 23
You are building a dashboard to monitor the performance of online advertisements. The data is stored in a Snowflake table 'AD PERFORMANCE' with columns like 'AD ID', 'IMPRESSIONS', 'CLICKS', 'CONVERSIONS', 'COST' , and 'DATE'. You want to display a trendline of the daily Click-Through Rate (CTR = CLICKS / IMPRESSIONS) and Cost Per Conversion (CPC = COST / CONVERSIONS). Some days might have zero impressions or conversions. To handle these cases and avoid errors or misleading visualizations, which of the following SQL code snippets BEST calculates CTR and CPC safely within a view or CTE used by your dashboard? (Assume appropriate data types for columns).
Answer: C
Explanation:
The function in Snowflake (D) is specifically designed to handle division by zero by returning NULL. It is the cleanest and most Snowflake-idiomatic way to address this issue. (B) and 'CASE' statement(E) are valid, but slightly more verbose. Will not work in the scenario when one the columns being divided is null. Option A will result in division by zero errors when IMPRESSIONS or CONVERSIONS are 0.
NEW QUESTION # 24
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?
Answer: D
Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.
NEW QUESTION # 25
A Data Analyst wants to transform query results. Which transformation option will incur compute costs?
Answer: D
Explanation:
In the Snowflake Snowsight interface, it is critical to distinguish between UI-level formatting and engine- level processing. Snowsight provides several client-side features that allow an analyst to change how data is displayed without re-executing the underlying SQL query or utilizing virtual warehouse credits.
Client-Side (No Compute Cost):
Formatting options such as adding thousand separators (Option A), adjusting the visible decimal precision (Option C), or changing the display format of dates and timestamps (Option D) are typically handled by the Snowsight web interface itself. These transformations are applied to the data that has already been retrieved into the browser's local result cache. Because they do not require the virtual warehouse to scan micro- partitions or perform new calculations, they do not incur additional compute costs.
Engine-Level (Incurs Compute Cost):
Sorting a column (Option B) is fundamentally different. While Snowsight allows you to click a column header to sort, this action frequently triggers a re-query or a secondary processing step if the entire result set is not already fully cached in the browser's memory. When you use "column options" to perform operations like sorting, filtering, or grouping on large datasets, Snowflake often has to leverage the virtual warehouse to reorganize the data. In the context of the Snowflake Data Analyst exam, sorting is identified as a transformation that requires active compute resources because the engine must evaluate the entire dataset to determine the new order of records.
Furthermore, even if a small result set is cached, complex sorting across large volumes of data necessitates warehouse involvement to ensure accuracy and handle "spilling" to local or remote storage if the sort operation exceeds available memory. Therefore, while visual "masks" are free, structural data reorganization like sorting is a compute-intensive task.
NEW QUESTION # 26
In Snowsight, why is it significant to create various chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?
Answer: D
Explanation:
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.
NEW QUESTION # 27
How do materialized views differ from regular views in terms of data access and storage?
Answer: D
Explanation:
Materialized views provide precomputed snapshots, differentiating them from regular views.
NEW QUESTION # 28
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