DAA-C01 Dumps With Exact Questions and Answers

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.

To determine the output of the final statement, a Data Analyst must perform a step-by-step trace of the code execution, following standard order of operations (inner-to-outer) and procedural logic within the Snowflake Scripting environment.

Step 1: Evaluating the innermost function, func2(10)

The definition for func2 takes a number p and returns p + 10.

Input : 10

Calculation : $10 + 10 = 20$

Result : 20

Step 2: Evaluating the next function, func1(20)

The definition for func1 takes a number v and returns v + 10.

Input : 20 (the result from Step 1)

Calculation : $20 + 10 = 30$

Result : 30

Step 3: Evaluating the procedure call, proc1(30)

The procedure proc1 takes an input variable named result. It then executes a conditional IF statement:

Conditional Check : IF (result > = 30)

Since the input is 30, the condition evaluates to True .

Action : The procedure executes RETURN result - 10.

Calculation : $30 - 10 = 20$

Summary of Logic:

The nested calls flow as follows: call proc1(30) $\rightarrow$ proc1 evaluates $30 \ge 30$ $\rightarrow$ proc1 returns $30 - 10$, which is 20 . If the initial input had been lower, such that the final result passed to the procedure was less than 30, it would have followed the ELSE branch and returned the original value. This question evaluates the analyst ' s ability to interpret modular code and procedural control flow, which are key components of the Data Transformation and Data Modeling domain in the SnowPro Advanced: Data Analyst exam.

Snowflake provides a powerful feature called Result Caching . When a query is executed, the results are persisted for 24 hours . To access the result set of a previously executed query without re-running the heavy computation (and thus saving credits), a Data Analyst can use the RESULT_SCAN table function.

The function RESULT_SCAN requires a Query ID as its input. The most common way to programmatically retrieve the ID of the most recently executed statement in the current session is by using the LAST_QUERY_ID() function. By wrapping this in the TABLE() operator, Snowflake treats the cached results as a virtual table that can be queried, filtered, or even joined with other tables.

Evaluating the Options:

Option B is incorrect because QUERY_HISTORY returns metadata about queries (like start time, user, and status) but does not return the actual data records produced by those queries.

Option C is incorrect as DESC (Describe) is used to see the columns and data types of an object, not to fetch its row data.

Option D is incorrect; while LAST_QUERY_ID(-1) might successfully retrieve a string representing a previous ID in some contexts, it does not actually " access the results " or display the data.

Option A is the 100% correct syntax. It is the standard method used by Data Analysts to perform post-processing on a large result set or to recover a result set if the worksheet was accidentally cleared. This is a key part of the Data Analysis domain, specifically regarding performance optimization and the utilization of Snowflake ' s unique caching layers.