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| Section | Objectives |
|---|---|
| Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
| Data Transformation and Analysis | - Analytical workloads
|
| Security, Governance, and Data Sharing | - Access control and security
|
| Data Loading and Unloading | - Data ingestion methods
|
| Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
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NEW QUESTION # 34
What are the PRIMARY reasons for using integrity constraints on Snowflake tables? (Select TWO).
Answer: A,E
Explanation:
Understanding how Snowflake handles integrity constraints is vital, as it differs significantly from traditional transactional databases like PostgreSQL or SQL Server. In Snowflake, most constraints are not enforced by the system, with one major exception.
* Enforcement vs. Documentation: Snowflake does not enforce PRIMARY KEY, FOREIGN KEY, or UNIQUE constraints during data loading or updates. If you define a primary key, Snowflake will still allow duplicate values to be inserted. The primary reason for including these is for documentation and metadata (Option C), allowing data analysts and BI tools to understand the intended relationships and schema design.
* The NOT NULL Exception: The only integrity constraint that Snowflake actively enforces is NOT NULL (Option B). If a column is defined as NOT NULL, any attempt to insert or update a record with a null value in that column will result in an error.
Evaluating the Options:
* Options A and D are incorrect because Snowflake does not actually enforce these constraints; it merely stores them as metadata.
* Option E is incorrect because while keys can be used for clustering, defining them as constraints is not a prerequisite for specifying them as clustering keys.
* Options B and C are the 100% correct reasons. They represent the practical application (enforcing data quality for nulls) and the architectural application (providing context for the data model).
NEW QUESTION # 35
Which actions are typically part of responding to data import errors in Snowflake? (Select all that apply)
Answer: A,B,D
Explanation:
Responding to data import errors involves identifying error sources, resolving inconsistencies, and analyzing logs for resolution.
NEW QUESTION # 36
What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)
Answer: B,C
Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.
NEW QUESTION # 37
You are working with a Snowflake table 'ORDERS that contains order data in a VARIANT column named 'ORDER DETAILS'. The 'ORDER DETAILS column contains JSON objects with nested arrays of product information, including 'product_id', 'quantity', and 'price'. You need to calculate the total revenue for each order. Which of the following SQL snippets correctly calculates the total revenue for each order using LATERAL FLATTEN and aggregation?
Answer: A,B
Explanation:
Snowflake requires explicit casting to numeric datatypes when performing arithmetic operations on VARIANT data. Options A and B do not cast the 'quantity' and 'price' fields to numbers, which would result in incorrect calculations. Option E uses a deprecated ' TO_NUMBER function.
NEW QUESTION # 38
A Data Analyst needs to temporarily hide a tile in a dashboard. The data will need to be available in the future, and additional data may be added. Which tile should be used?
Answer: C
Explanation:
In Snowsight, managing dashboard layouts requires an understanding of how tiles (queries or visualizations) are stored versus how they are displayed. When an analyst wants to remove a tile from the visible dashboard grid without destroying the underlying query logic or historical configuration, the Unplace action is the correct functional choice.
When a tile is unplaced, it is removed from the dashboard's active layout but remains part of the dashboard's
"library" of available content. This is a critical distinction from the Delete action (Option C), which permanently removes the tile and its associated SQL code from the dashboard object. Unplacing allows the analyst to "archive" the work temporarily. Because the tile still technically exists within the dashboard's metadata, any new data added to the underlying tables will still be processed by the query whenever the tile is eventually placed back onto the grid.
Evaluating the Options:
* Option A (Show/Hide) is not a standard standalone command for dashboard tile management in Snowsight; visibility is typically managed through placement on the grid.
* Option B (Duplicate) creates a second copy of the tile. While this preserves the data, it does not satisfy the requirement to "hide" the current tile; it actually adds more clutter to the dashboard.
* Option C (Delete) is incorrect because the prompt specifies that the data and tile will need to be available in the future. Deleting would require the analyst to rewrite the SQL and reconfigure the visualization from scratch.
* Option D is the 100% correct answer. Unplacing is the "soft-remove" feature of Snowsight. It preserves the tile in the "Unplaced Tiles" sidebar, allowing for quick restoration at a later date. This feature is essential for analysts who need to manage evolving reporting requirements where certain metrics may only be relevant seasonally or during specific business cycles.
NEW QUESTION # 39
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