DAA-C01 Exam Dumps - SnowPro Advanced: Data Analyst Exam

In Snowsight, Snowflake's modern web interface, interactivity is driven by Dashboard Filters. When an analyst defines a filter, it serves as a dynamic variable that users can manipulate to update visualizations. To incorporate these filters into the SQL logic of a dashboard tile, Snowflake utilizes the colon prefix (:) syntax.

Referencing the filter as :branch_region tells the Snowflake query engine to replace that placeholder with the value(s) selected in the dashboard UI at runtime. This is consistent with how other system filters, such as :daterange, are implemented to ensure all tiles on a dashboard remain synchronized. Using this syntax allows the same query to be repurposed for different regions without manual code changes, significantly reducing the maintenance burden for the 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.

Snowsight provides special System Keywords that allow analysts to create dynamic, interactive dashboards without hard-coding dates. Two of the most critical keywords are :daterange and :datebucket.

The :daterange keyword is designed to filter the volume of data based on a time period selected by the user in the dashboard UI (e.g., "Last 30 days" or "Current Year"). Because it acts as a filter on the underlying data, it must be placed in the WHERE clause of the SQL statement (e.g., WHERE created_at = :daterange). This ensures that only the records within the selected timeframe are processed by the query.

The :datebucket keyword is used for time-series aggregation. It allows the user to dynamically change the granularity of the data—for example, switching a chart from "Daily" to "Monthly" views without rewriting the query. To achieve this, :datebucket is used inside a date-truncation function in the SELECT list and, crucially, must be included in the GROUP BY clause to correctly aggregate the metrics (e.g., GROUP BY 1 or GROUP BY :datebucket).

Evaluating the Options:

Option A is incorrect because :datebucket is for grouping/truncation, not for filtering in a WHERE clause.

Option B is incorrect because :daterange is a filter (returning a range), not a scalar value suitable for a SELECT list.

Option C is incorrect for the same reasons as A and B.

Option D is the 100% correct answer. It follows the standard Snowsight design pattern: :daterange restricts the data rows in the WHERE clause, while :datebucket defines the temporal aggregation level in the GROUP BY clause.