One of the standout features of the Snowsight interface is its ability to perform automatic Data Profiling . When a Data Analyst executes a query, Snowflake doesn ' t just return a raw grid of data; it analyzes the result set to provide immediate visual insights.

When you click on a column header in the results pane, a summary statistics panel appears. For numeric, date, and time columns , Snowflake automatically generates a histogram (Option A). This histogram provides a visual representation of the data distribution, allowing the analyst to quickly identify patterns, concentrations of values, or significant outliers without writing additional SQL code.

Evaluating the Options:

Option B: While a histogram is a type of frequency distribution, Option A is more accurate because Snowsight also provides these visualizations for date and time types, not just integers/floats.

Option C: While MIN and MAX values are displayed in the summary panel, they are text-based statistics, not the " visualized " contextual statistic (the histogram) emphasized in the question.

Option D: " Key distribution " is not a standard visualization term used in the Snowsight profiling tool.

Option A: Is the 100% correct answer. It highlights the breadth of the profiling tool (covering numbers, dates, and times) and the specific visual element (the histogram) that makes exploratory data analysis significantly faster for a Data Analyst.

Snowflake Cortex ML functions, such as FORECAST, return a tabular result set when called using the instance method syntax (e.g., CALL modelX!FORECAST(...)). While this output is visible in the Snowsight results pane, the CALL statement itself cannot be used directly as a subquery within a standard INSERT INTO or CREATE TABLE AS SELECT (CTAS) statement.

To persist the results of a model ' s prediction into a permanent table (tableX), the Data Analyst must utilize the RESULT_SCAN table function. Snowflake stores the results of every query and function call in a temporary cache for 24 hours. The RESULT_SCAN function allows you to treat that cache as a queryable table.

The standard workflow is:

Execute the forecast: CALL modelX!FORECAST(FORECASTING_PERIODS = > 12);

Immediately after, use the LAST_QUERY_ID() function to identify the query that generated the forecast results.

Create the table by querying that result set: CREATE TABLE tableX AS SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID()));

Evaluating the Options:

Option A is incorrect because the CALL syntax does not support a direct INTO clause for table creation.

Option B is incorrect because passing a table as an argument is part of the training or input phase, not the output persistence phase.

Option D is overly complex and contains non-standard terminology ( " List the cache content " ).

Option C is the 100% correct answer. It reflects the required " post-processing " step in the Snowflake Data Cloud to bridge the gap between procedural model calls and relational table storage.

When working within the Snowsight interface, the transition from a standalone worksheet to a dashboard component involves a change in how the underlying SQL and its associated metadata are managed. When a worksheet is converted or used to create a dashboard, the ownership and sharing model shifts to the dashboard level.

According to Snowflake ' s documentation on Snowsight collaboration, when a user creates a dashboard from a worksheet that was previously shared with others, the original worksheet ' s individual share settings are essentially superseded by the dashboard ' s own permissions. In many workflow scenarios within the UI, once the worksheet is finalized into a dashboard tile, the direct, independent access to that specific worksheet is severed for the original " sharees. " Their permissions on that specific worksheet are revoked to prevent conflicting edits between the standalone version and the dashboard-integrated version.

Evaluating the Options:

Option A is incorrect because Snowsight manages the lifecycle of worksheets used in dashboards as part of the dashboard object; independent sharing of the underlying worksheet is typically disabled or revoked.

Option B and C are distractors; there is no mechanism in Snowsight that grants " additional " access or " temporary " loss during the creation process.

Option D is the 100% correct answer based on the SnowPro Data Analyst standard regarding Snowsight object ownership and sharing lifecycle. To allow others to see the work, the Analyst must now share the Dashboard itself, rather than relying on the previous worksheet-level permissions. This ensures a " single source of truth " for the visualization ' s logic.

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.

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.

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.

Understanding how Snowflake aggregate functions like MIN() and MAX() handle numerical data and NULL values is fundamental for accurate Data Analysis . In this scenario, we have a table with five records distributed across two departments.

The MIN() function returns the smallest non-null value in the specified column across all rows in the group (or the entire table, if no GROUP BY is present). Looking at the salary column in the employees table, the values are: 10000, 9000, 8000, 15000, and NULL. The NULL value is ignored by the calculation. Among the remaining numerical values, 8000 is the smallest. Therefore, MIN_VAL will be 8000 .

The MAX() function operates similarly, returning the largest non-null value from the set. Comparing the same list of numerical values—10000, 9000, 8000, and 15000—the largest value is clearly 15000. Consequently, MAX_VAL will be 15000 .

Evaluating the Options based on the exhibit (image_8ca0df.png):

Option A incorrectly identifies the minimum and maximum based on the employee_id column (where 2000 is max and 900 is min), rather than the requested salary column.

Option B is incorrect as it seems to mix values from different columns or specific rows.

Option C is incorrect because it mistakenly suggests that MIN() returns NULL if a NULL value is present in the column. In Snowflake, standard aggregate functions (except for specialized ones like ARRAY_AGG or certain window functions with specific clauses) skip NULL values entirely.

Option D is the 100% correct output. It displays MIN_VAL as 8000 and MAX_VAL as 15000, which accurately reflects the mathematical minimum and maximum of the non-null entries in the salary column.

This behavior is consistent across all relational databases adhering to ANSI SQL standards, which Snowflake follows for these core aggregate operations.

This scenario highlights the rules governing Shared Databases and Cross-Database Joins in Snowflake. When a user acquires data from the Snowflake Marketplace, it arrives in their account as a " Shared Database. "

A critical restriction of shared databases is that they are read-only . Users (including Data Analysts and even AccountAdmins) cannot create new objects—such as tables, schemas, or views—directly inside a database created from a share. Therefore, Options A, C, and D are architecturally impossible because they all involve trying to write a new view into the ADDRESS_DATA database.

To combine the shared data with internal data (like the ORDERS table in GLOBAL_DWH), the Analyst must create the view in a database where they have CREATE VIEW privileges and that is writeable . Since GLOBAL_DWH is an internal database owned by the organization, it is the appropriate location to host the logic that joins local data with external enrichment data.

Evaluating the Options:

Option A and C are incorrect because you cannot create objects in a database created from a share.

Option D is incorrect because while IMPORTED_PRIVILEGES are necessary to view the data, they do not grant the ability to create objects within the share.

Option B is the 100% correct answer. By creating the view in the GLOBAL_DWH database (or any other local, non-shared database), the Analyst can successfully reference ADDRESS_DATA.OPENADDRESS and GLOBAL_DWH.PUBLIC.ORDERS in a single SQL statement, effectively creating an enriched data layer for the organization.

In Snowsight , the Scorecard chart type is specifically designed to highlight a single key metric (the " Value " ) and optionally compare it against a static or dynamic benchmark (the " Comparison " ). This visualization is a core component of the Data Presentation and Data Visualization domain, as it provides an immediate " at-a-glance " status for high-level KPIs.

1. Interpreting the Scorecard Components:

Primary Value: The large, bold number (38) represents the actual value calculated by the underlying query. In this scenario, it is the company ' s average employee age.

Secondary Metric (The Percentage): When a scorecard is configured with a comparison value, Snowsight automatically calculates the percentage difference between the primary value and that comparison.

Visual Indicators: The green upward arrow indicates that the primary value is higher than the comparison value. If the value were lower, the arrow would point downward and typically appear in red.

2. The Mathematical Calculation:

The 12% shown in the exhibit is the result of the percentage change formula:

Evaluating the Options:

Options A and D are incorrect because they misidentify the large number (38) as the " comparison. " In Snowflake ' s UI, the large number is always the primary metric being tracked, not the target it is being measured against.

Option B is incorrect because the percentage in a scorecard represents relative difference, not a statistical measure like standard deviation.

Option C is the 100% correct interpretation. It correctly identifies 38 as the current company value and 12% as the calculated difference from the industry benchmark of 34. This level of visual literacy is expected of a SnowPro Advanced: Data Analyst to ensure dashboard insights are communicated accurately to stakeholders.

The GENERATOR table function is used in Snowflake to produce a synthetic result set, typically for testing or creating dummy data. However, the GENERATOR function requires at least one of two parameters to be explicitly set to a positive value to produce any rows: ROWCOUNT or TIMELIMIT.

In the provided query, the user has set TIMELIMIT = > NULL. In Snowflake ' s SQL implementation for the GENERATOR function, NULL is treated as a zero or an undefined limit. Furthermore, since the ROWCOUNT parameter is omitted, it defaults to zero. Because neither a positive duration nor a positive row count has been specified, the generator engine has no instruction on how many rows to build.

Evaluating the Results:

Option A is a distractor; while one might think NULL means " no limit " (infinity), the GENERATOR function is designed to be finite and safe.

Option B is incorrect; the number 12 inside the RANDOM(12) function is merely a seed value for the random number generator, ensuring the output is reproducible. It has no impact on the number of rows produced by the FROM clause.

Option C is incorrect; 10 is just a boundary for the UNIFORM function.

Option D is the 100% correct answer. Because the query specifies TIMELIMIT = > NULL and fails to provide a ROWCOUNT, Snowflake will execute the query successfully but return a result set containing 0 rows . This is a common " trick " question in the Data Analysis domain to test an analyst ' s familiarity with table function parameters.