Snowflake DAA-C01 Based on Real Exam Environment

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.

The SAMPLE clause (or TABLESAMPLE) is used in Snowflake to return a subset of rows from a table. When performing analysis on massive datasets, sampling allows for faster query execution and reduced credit consumption while still providing a statistically representative view of the data.

There are two primary methods of sampling in Snowflake: BERNOULLI (row-based) and BLOCK (partition-based). The query in this question uses BLOCK sampling, which selects a specific percentage of micro-partitions (blocks) rather than individual rows. This method is significantly faster for very large tables because it avoids the overhead of scanning every single row within a block; it either includes the entire block or skips it entirely.

Evaluating the Syntax:

Probability: The value inside the parentheses (0.012) represents the probability percentage for inclusion. Unlike some systems that might use decimals (where 1.0 = 100%), Snowflake treats this number as a direct percentage. Therefore, 0.012 is exactly 0.012%, not 1.2%.

Repeatable/Seed: The REPEATABLE clause (or SEED) followed by a number (99992) ensures that the sampling is deterministic. If the underlying data does not change, running this same query multiple times with the same seed will return the exact same "random" subset of blocks.

Evaluating the Options:

Options A and C are incorrect because they misinterpret the probability 0.012 as 1.2%.

Option D is incorrect because it mistakenly identifies the seed number 99992 as a target row count.

Option B is the 100% correct answer as it accurately identifies the sampling method (BLOCK), the correct percentage probability (0.012%), and the role of the seed (99992).

Snowsight, the modern web interface for Snowflake, introduces robust collaboration features through shared worksheets. Understanding the synchronization and versioning logic is critical for the Data Presentation and Data Visualization domain.

When a worksheet is shared with collaborators, Snowflake enables real-time collaboration (Option D). This means that as one user types or modifies SQL code, those changes are immediately visible to all other users who have the worksheet open. This functionality is similar to co-authoring in modern document editors, allowing data teams to debug or develop complex queries synchronously without the need for manual copy-pasting or constant screen sharing.

Furthermore, Snowflake manages the execution history of shared worksheets through a structured versioning system (Option C). If multiple users are interacting with the same worksheet, Snowflake does not overwrite a single "master" state during execution. Instead, every time a user executes the worksheet (or a portion of it), Snowflake creates a new version of the worksheet content associated with that specific run. This ensures that the code used to generate a particular result set is preserved and tied to that specific Query ID in the history.

Evaluating the Options:

Option A is incorrect because while users can see current edits, viewing a full, navigable history of "past versions" is not a standard feature in the same way it is in dedicated version control systems like Git.

Option B is incorrect as worksheet sharing is restricted to users within the same Snowflake account. Sharing across accounts requires the use of Data Shares or Private Listings.

Option E is incorrect because Snowflake preserves the history rather than overwriting it, allowing analysts to audit who ran what code and when.

Options C and D are the 100% correct answers. They define the synchronous and persistent nature of Snowsight collaboration, ensuring that teams can work together effectively while maintaining an immutable trail of executions.

This scenario requires a specific combination of Snowflake's extensibility features: the ability to run Python (pandas) code, the ability to return multiple rows for a single input (tabular output), and high performance through vectorization.

A User-Defined Table Function (UDTF) is the correct architectural choice when an input value needs to be expanded into a set of rows. While a standard UDF returns exactly one value per input row, a UDTF can return zero, one, or many rows, making it ideal for "parsing" or "splitting" tasks. By using a Vectorized UDTF (specifically utilizing the Python handler with pandas), the Analyst can process blocks of rows as pandas DataFrames or Series. This is significantly more efficient than processing one row at a time because it reduces the overhead of the Snowflake-to-Python execution engine.

Evaluating the Options:

Option A is incorrect because a standard UDF can only return a single scalar value per input row, making it impossible to "return multiple rows of parsed output."

Option C is incorrect because External Functions are used to call code hosted outside of Snowflake (like an AWS Lambda). While they can process data, they introduce network latency and are not the native way to run pandas code for internal joins.

Option D is incorrect because Stored Procedures are generally used for administrative tasks and procedural logic. While they can return tables, they cannot be directly "joined" with other tables in a standard SELECT statement in the same way a UDTF can.

Option B is the correct choice. It satisfies the requirement for pandas integration, provides the necessary multi-row output via the process and end_partition methods, and allows the analyst to use the LATERAL keyword to join the parsed results directly with existing tables in a single SQL statement.