Snowflake DAA-C01 Exam Dumps - Actual Questions Answers

In Snowsight (Snowflake's web interface), Data Analysts can create interactive dashboards and worksheets using Filters. When you define a filter (such as a Date Range or a list of Regions), you must assign it a SQL Keyword.

This "SQL Keyword" acts as a variable placeholder within your SQL code. For example, if you create a filter for "Customer Region" and set the SQL keyword to :my_region, you can then write a query like: SELECT * FROM sales WHERE region = :my_region. When a user interacts with the UI and selects "North America" from the dropdown, Snowsight automatically injects "North America" into every instance where :my_region appears in the worksheet's SQL.

Evaluating the Options:

Option A is incorrect because the keyword is a label/variable, not the underlying function code.

Option C and Option D are incorrect as they confuse the data source of the filter values with the reference name used in the SQL code.

Option B is the correct answer. The SQL Keyword is specifically the identifier (prefixed with a colon in the code) that allows the analyst to link the UI element (the filter) to the execution logic of the query. This is a fundamental skill for the Data Presentation and Data Visualization domain, ensuring reports are dynamic and user-friendly.

In Data Analysis, correlation refers to a statistical relationship between two variables. When analyzing a time-series chart like the one provided, a Data Analyst looks for patterns in how the two categories—"Enterprise" (blue line) and "Pro Edition" (yellow line)—move in relation to one another over the X-axis (Year).

A Positive Correlation would be indicated if both lines generally moved in the same direction at the same time (e.g., when Enterprise sales increase, Pro Edition sales also increase). A Negative Correlation (or inverse correlation) would be shown if the lines moved in opposite directions consistently (e.g., when one peaks, the other troughs).

Looking closely at the provided exhibit, the fluctuations for both editions are highly erratic and appear independent of each other. For instance, around the year 2008, the Pro Edition (yellow) shows a significant peak while the Enterprise edition (blue) experiences a sharp decline. Conversely, in other sections of the chart, they both dip or rise simultaneously by chance, but there is no sustained, predictable pattern of movement. The peaks and valleys do not align in a way that suggests one variable's movement is tied to the other.

Statistically, this lack of a discernible relationship indicates a Correlation Coefficient near zero. In the context of the Snowflake Snowpro Advanced: Data Analyst exam, identifying "No Correlation" is a key skill for interpreting Snowsight visualizations. It tells the analyst that the factors driving sales for the Enterprise tier are likely distinct from those driving the Pro Edition, and they should be analyzed as independent segments rather than interdependent variables. Therefore, based on the visual evidence of random, non-synchronous movement across the timeline, the only supported conclusion is that there is no correlation.

Descriptive analytics is the process of using historical data to understand "what happened." This typically involves summarizing large datasets into interpretable chunks using central tendency, dispersion, and frequency measures.

AVG (Average) is a cornerstone of descriptive statistics. It provides the arithmetic mean of a numeric column, allowing an analyst to understand the "typical" value within a dataset (e.g., Average Order Value).

APPROX_COUNT_DISTINCT is a descriptive tool used to understand the volume of unique entities within a dataset (e.g., "How many unique customers visited the site?"). Similar to HLL mentioned earlier, this function provides a fast summary of data volume and variety, which is a primary goal of the descriptive phase of analysis.

Evaluating the Options:

Options A and B (REGR_INTERCEPT and REGR_SLOPE) are used for linear regression. These fall under Predictive Analytics, as they are used to model relationships and predict future outcomes, rather than just describing current data.

Option C (ROW_NUMBER) is a window function used for data ranking and ordering, but it does not provide a descriptive summary of the dataset's characteristics.

Options D and E are correct because they provide summary statistics (mean and cardinality) that define the "state" of the data, which is the definition of descriptive analytics.