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

In Snowsight, worksheets and dashboards are tightly integrated to facilitate a smooth transition from data exploration to formal reporting. When a Data Analyst has already done the "heavy lifting"—cleaning and preparing data within standalone worksheets—Snowflake provides a direct way to convert those worksheets into dashboard tiles.

The "least operational overhead" method is to Move the existing worksheets into the dashboard. When you are in the dashboard view, you can select the option to add a tile and then choose an existing worksheet from your folders. This "Move" or "Add from Worksheet" action preserves the entire SQL statement, any formatting applied to the results, and the visualization settings (charts) that were already configured in the worksheet.

Evaluating the Options:

Option B and C are technically possible but require significantly more effort (manual copy-pasting, switching windows, re-naming tiles). This increases the risk of human error and is not considered a high-efficiency workflow in Snowsight.

Option D is the most complex and overhead-intensive option, requiring the analyst to sift through system logs to recover code they already have access to in their UI.

Option A is the 100% correct answer. By moving or importing the worksheet directly into the dashboard as a tile, the analyst maintains the link between the development work and the final presentation with just a few clicks. Note that once a worksheet is moved into a dashboard, it essentially becomes a "tile" and is managed as part of the dashboard object.

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.

In the realm of Data Presentation and Data Visualization, selecting the appropriate chart type is critical for revealing the underlying structure of data. When an analyst is tasked with identifying "natural clusters," they are looking for groups of data points that share similar characteristics across two or more dimensions.

A Scatter Plot is the primary visualization tool for this purpose. By plotting two numeric variables (e.g., ORDER_AMOUNT on the X-axis and CUSTOMER_LOYALTY_SCORE on the Y-axis), the scatter plot allows the human eye to immediately detect density. If the data points form distinct "clouds" or groupings, these represent the clusters the analyst is searching for. In Snowsight, scatter plots are highly effective for exploratory data analysis, as they allow analysts to visualize the relationship between variables and spot outliers that may skew statistical summaries.

Evaluating the Options:

Option A is incorrect because Line Charts are designed to show trends over time (sequential data). They connect points in a specific order, which would obscure clustering logic by forcing a temporal connection between unrelated data points.

Option B is incorrect because a Heat Grid (or Heatmap) is best used for comparing categories across two dimensions using color intensity. While it can show high-density areas, it aggregates data into "bins" or cells, which can mask the fine-grained distribution required to see natural, point-based clusters.

Option C is incorrect because Bar Charts are used for comparing discrete categories or showing distributions within a single dimension. They do not effectively show the correlation between two continuous variables needed for cluster identification.

Option D is the 100% correct answer. The Scatter Plot is the standard analytical graphic for cluster detection and correlation analysis within the Snowflake Snowsight interface, providing the necessary granularity to see how individual orders or customers group together in a multi-dimensional space.

Snowflake utilizes a unique architecture known as Zero-Copy Cloning, which allows users to create a replica of a table, schema, or entire database without physically duplicating the underlying data files (micro-partitions). When you execute the CLONE command, Snowflake simply creates new metadata that points to the existing micro-partitions of the source object.

Because the data is not physically copied, the clone operation is nearly instantaneous and, crucially, incurs no additional storage costs at the moment of creation. Storage costs only begin to accumulate for the clone when the data in the source or the clone diverges—for example, if rows are updated or deleted in the clone, Snowflake creates new micro-partitions to store the changed data while preserving the original state in the source.

Evaluating the Options:

Option A (LIKE) only copies the column definitions and structure of the table. It does not copy the data itself, so while it doesn't incur storage, it also doesn't provide the "data" requested by the prompt.

Option B (CTAS - Create Table As Select) performs a full deep copy of the data. This creates entirely new micro-partitions, which immediately increases the storage footprint and associated costs.

Option D is a filtered CTAS operation. While it may result in less data than the full table, it still involves creating new physical storage for the 2019 records, thus incurring additional costs.

Option C is the 100% correct answer. It uses the CLONE keyword, which is the specific Snowflake feature designed to provide a full dataset for dev/test environments with zero initial storage impact. This is a core competency in the Data Transformation and Data Modeling domain.

Managing complex pipelines involving multiple dependent tasks requires an understanding of Task Graphs (DAGs). In Snowflake, a DAG is a series of tasks with parent-child relationships. If a parent task (like STEP10) fails, Snowflake automatically cancels the execution of all child tasks (like STEP11 and STEP12) to prevent data corruption or inconsistent states.

To recover from such a failure, Snowflake provides the RETRY LAST clause within the EXECUTE TASK command. The critical nuance here is that you do not target the individual failed task; instead, you target the root task of the graph (in this case, ETLDAILY). When you run EXECUTE TASK RETRY LAST;, Snowflake identifies the most recent failed run of that graph and intelligently resumes execution starting from the failed task (STEP10) and continues through all subsequent dependent tasks that were previously skipped.

Evaluating the Options:

Option A is incorrect because attempting to execute just STEP10 would treat it as a standalone run and would not automatically trigger the dependent logic of the ETLDAILY graph structure.

Options C and D are incorrect because RESUME is a DDL command used to change a task's state from SUSPENDED to STARTED (enabled). It does not trigger an immediate execution or a retry of a failed run; it merely allows the task to run on its next scheduled interval.

Option B is the 100% correct answer. It utilizes the dedicated Snowflake mechanism for graph recovery, ensuring that the entire pipeline is completed starting from the point of failure without re-running successfully completed earlier steps.