DAA-C01 SnowPro Advanced: Data Analyst Exam Questions and Answers

When working with very large tables, calculating the exact number of unique combinations of columns (cardinality) using COUNT(DISTINCT ...) is a resource-intensive operation. It requires the Snowflake query engine to keep an exhaustive list of every unique pair encountered in memory, which can lead to high credit consumption and performance bottlenecks.

To provide a "very quick estimate," Snowflake utilizes the HyperLogLog (HLL) algorithm. The function HLL(column1, column2, ...) returns an HLL state (a binary representation) that can be used to estimate the number of distinct values with a high degree of accuracy and minimal computational overhead. This is part of Snowflake's suite of approximate aggregation functions, which are essential for Data Analysis on massive datasets where a 1% margin of error is acceptable in exchange for significantly faster results.

Evaluating the Options:

Option A is syntactically incorrect; DISTINCT cannot be used in that position with COUNT.

Option C is a valid query but will be significantly slower and more expensive than an HLL estimate on a "very large table".

Option D simply counts the total number of non-null rows, which does not represent the "number of different values" (cardinality).

Option B is the 100% correct answer. It specifically addresses the requirement for a "quick estimate" using the industry-standard probabilistic counting method built into Snowflake.

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a worksheet that was previously shared with others, the original worksheet's individual share settings are essentially superseded by the dashboard's own permissions.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.

In Snowflake, "spilling" occurs when the intermediate data required to process a query exceeds the available memory (RAM) of the virtual warehouse. When this happens, Snowflake "spills" the data first to the warehouse's local SSD and, if that fills up, to remote storage (e.g., S3 or Azure Blob). This significantly degrades performance due to increased I/O latency.

To resolve this without increasing costs (i.e., without scaling up to a larger warehouse size), an analyst must reduce the memory footprint of the query.

CTEs vs. Temporary Tables: While Common Table Expressions (CTEs) are excellent for readability, they are often treated as logical entities that the optimizer may re-evaluate multiple times or struggle to keep in memory during massive joins.

Batch Processing: By splitting a single monolithic query into multiple steps and storing intermediate results in temporary tables, the analyst forces the engine to clear its memory between steps. This process acts as a "checkpoint," effectively processing the data in smaller, more manageable batches that fit within the current warehouse's memory limits.

Evaluating the Options:

Option A is incorrect because spilling is a compute-resource issue, not a storage-lock or contention issue.

Option B is incorrect because materialized views and clustering incur background credit costs for maintenance, violating the "without increasing costs" constraint.

Option C is incorrect because external tables are generally slower than native Snowflake tables due to the lack of proprietary metadata and micro-partitioning.

Snowflake Cortex ML functions, such as the Forecasting and Anomaly Detection models, are designed to be "black boxes" that provide automated machine learning capabilities directly within SQL. However, for a Data Analyst to trust and validate these models, Snowflake provides specific Object Methods (invoked with the ! operator) to inspect the model's internal logic and performance.

The !EXPLAIN_FEATURE_IMPORTANCE() method is specifically designed to provide transparency into how the model reached its conclusions. When invoked on a trained forecast model, it returns a result set showing which features (such as exogenous variables or time-based components like seasonality and trend) had the most significant impact on the predicted values. This is a critical step in the Data Analysis workflow to ensure that the model is not relying on "noise" or irrelevant data points.

Evaluating the Options:

Option A (SHOW_EVALUATION_METRICS) is used to retrieve accuracy statistics like MSE (Mean Squared Error) or MAPE (Mean Absolute Percentage Error) from the training phase, but it does not explain the contribution of specific features.

Option B (SHOW_TRAINING_LOGS) is not a standard Cortex ML method; logging details are typically handled internally or through different system views.

Option D (FORECAST) is the primary method used to actually generate the future predictions once the model is trained; it outputs the forecast itself, not the underlying component importance.

Option C is the correct answer as it is the dedicated method for model interpretability, allowing analysts to see the "why" behind the forecast by quantifying the influence of each input variable. This aligns with Snowflake's focus on "Explainable AI" within the Data Cloud.

In the Snowflake ecosystem, Parquet is treated as a semi-structured data format. When you stage a Parquet file, Snowflake does not automatically parse it into multiple columns like it might with a flat CSV file. Instead, the entire content of a single row or record is loaded into a single VARIANT column, which is referenced in SQL using the positional notation $1.

The fundamental mistake often made—and represented in Option A—is treating Parquet as a delimited format where $1, $2, and $3 refer to different columns. In Parquet ingestion, columns $2 and beyond will return NULL because the schema is contained within the object in $1.

To successfully "shred" or flatten this semi-structured data into a relational table with separate columns, an analyst must use path notation. This involves referencing the root object ($1), followed by a colon (:), and then the specific element key (e.g., $1:o_custkey). Furthermore, because the values extracted from a Variant are technically still Variants, they must be explicitly cast to the correct data type using the double-colon syntax (e.g., ::number, ::date) to ensure they land in the target table with the correct data types.

Evaluating the Options:

Option A is incorrect because it uses positional references ($2, $3, etc.) which are only valid for structured files like CSVs.

Option B is incorrect because it attempts to reference keys directly without the required stage variable ($1) and colon separator.

Option D is incorrect as it uses a non-standard parse() function that does not exist for this purpose in Snowflake SQL.

Option C is the 100% correct syntax. It correctly identifies that the Parquet data resides in $1, utilizes the colon to access internal keys, and applies the necessary type casting. This specific method is known as "Transformation During Ingestion" and is a core competency for any SnowPro Advanced Data Analyst.

When working with "Big Data" at the scale of trillions of rows, calculating an exact count of unique values using COUNT(DISTINCT column) is extremely resource-intensive. This is because Snowflake must keep track of every unique value encountered to ensure no duplicates are counted, leading to high memory usage and long execution times (often referred to as "spilling to disk").

To solve this, Snowflake provides HyperLogLog (HLL) functions. HLL(*) (or specifically HLL_ACCUMULATE and HLL_ESTIMATE) allows an analyst to estimate the cardinality (the number of unique elements) with a very small, known margin of error (typically around 1%). This is significantly faster and uses far fewer credits than an exact count because it uses a probabilistic algorithm rather than a state-heavy tracking mechanism.

Evaluating the Options:

Option A is technically correct for small datasets but is highly inefficient for trillions of rows, directly contradicting the "efficiently" requirement of the question.

Option C is a distractor; while Snowflake has various SYSTEM$ functions, SYSTEM$ESTIMATE is not a standard function for cardinality.

Option D is a formula that doesn't target cardinality but rather a ratio (density).

Option B is the correct answer. The HLL family of functions is the industry standard within Snowflake for high-performance cardinality estimation on massive datasets.

One of the most powerful features of the Snowsight interface for a Data Analyst is the automatic data profiling provided in the results pane. Snowflake automatically calculates statistics and visual distributions for the result set of any query executed in a worksheet, provided the result set is not excessively large.

When the Analyst views the query results, they can simply click on the column header for the "total amount" column. This action opens a summary pane that displays key descriptive statistics such as the mean, sum, and a histogram showing the frequency distribution of the values in that specific column. This allows for immediate visual analysis of data skew, outliers, or common ranges without requiring the analyst to write additional SQL or move the data to an external visualization tool.

Evaluating the Options:

Option A is the Correct answer. This is the fastest, built-in way to perform "exploratory data analysis" (EDA) on a result set within the UI.

Option B and C are incorrect because while Snowsight does have a "Chart" tab, creating a chart requires manual configuration and is a separate step from the automatic profiling features found in the column headers.

Option D is incorrect because calling the WIDTH_BUCKET function would require the Analyst to run the query again with modified SQL logic, which explicitly contradicts the requirements of the question.

This feature significantly enhances the Data Analysis workflow by providing "at-a-glance" insights into data quality and distribution directly within the development environment.

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.

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.

In data modeling and analysis, "rotating" data is a common task used to normalize datasets for reporting or visualization. The operation of taking multiple columns (like the individual months in the source image) and turning them into values within a single column (like the "MONTH" column in the target image) is specifically known as unpivoting.

The UNPIVOT relational operator in Snowflake allows an analyst to transform a "wide" table format into a "narrow" (or "long") table format. In the wide format shown in the first image, data is distributed across columns named JAN, FEB, MAR, and APRIL. While this is often easier for humans to read in a spreadsheet, it is difficult to query for trends. By applying UNPIVOT, Snowflake collapses these columns into two new ones: one for the name of the original column (the attribute, such as "MONTH") and one for the value that was stored in that column (the metric, such as "SALES").

Evaluating the Options:

Option A (PIVOT) is the opposite of the required action. It is used to turn unique values from one column into multiple separate columns (narrow to wide), which is not what is happening in the exhibit.

Option C (INTERSECT) is a set operator that returns only the distinct rows that are present in both the first and second query results. It does not perform data rotation.

Option D (EXCEPT) is a set operator that returns rows from the first query that are not present in the second.

Option B is the 100% correct answer. It is the dedicated relational operator for converting column headers into row values, which is exactly the transformation required to move from the first image to the second. Mastering this operator is a critical skill for any SnowPro Advanced: Data Analyst when preparing messy source data for high-performance analytics.

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.

The ANY_VALUE function is an aggregate function used in Snowflake to bypass the requirement that all non-aggregated columns in a SELECT list must appear in the GROUP BY clause. In the provided query, the data is grouped by customer.id. Standard SQL would require customer.name to also be in the GROUP BY clause, even if every ID only has one name associated with it.

By using ANY_VALUE(customer.name), the analyst tells Snowflake to simply pick a value from the group. The core characteristic of this function is that it is non-deterministic. This means that if there are multiple different names for a single customer.id, Snowflake does not guarantee which one will be returned; it returns "any" value it finds most efficient to retrieve during processing. In most data modeling scenarios where a 1:1 relationship exists between an ID and a Name, ANY_VALUE is a performance-optimized alternative to using MIN() or MAX(), as it requires less computational overhead to identify a single representative value.

Evaluating the Options:

Option A is incorrect because ANY_VALUE only returns NULL if all values in the group are NULL.

Option C is incorrect because it describes the MIN() function, which is deterministic.

Option D is incorrect as it describes a median calculation (like MEDIAN()), which is a specific mathematical operation, not a "pick any" operation.

Option B is the 100% correct answer. It accurately defines the function's purpose: returning an arbitrary value from the group, acknowledging that the specific result is non-deterministic.

In Snowflake, table relationships established via constraints are managed at the metadata level. When a table that is referenced by a foreign key is renamed, Snowflake automatically maintains the integrity of that relationship by updating the internal metadata pointers.

Unlike some legacy database systems where a rename might break hardcoded references, Snowflake's architecture ensures that the Foreign Key in the son table remains valid. After executing ALTER TABLE mother RENAME TO father;, the constraint son_fk will be automatically updated by the system to reference the new table name, father. If you were to run a SHOW TRANSACTIONS or GET_DDL command on the son table after the rename, you would see that the REFERENCES clause now points to father(id).

Evaluating the Options:

Options A and B are incorrect because Snowflake is designed to prevent "orphaned" constraints during standard DDL operations like renaming. The relationship is preserved.

Option C is incorrect because Snowflake's query optimizer can still utilize these constraints for query rewrite and join elimination optimizations, provided the constraints are defined, regardless of the table's current name.

Option D is the 100% correct answer. This automatic update behavior is a core feature of Snowflake's metadata-driven approach to Data Transformation and Data Modeling, allowing for flexible schema evolution without breaking existing relational structures.

In the Snowflake Snowsight interface, it is critical to distinguish between UI-level formatting and engine-level processing. Snowsight provides several client-side features that allow an analyst to change how data is displayed without re-executing the underlying SQL query or utilizing virtual warehouse credits.

Client-Side (No Compute Cost):

Formatting options such as adding thousand separators (Option A), adjusting the visible decimal precision (Option C), or changing the display format of dates and timestamps (Option D) are typically handled by the Snowsight web interface itself. These transformations are applied to the data that has already been retrieved into the browser's local result cache. Because they do not require the virtual warehouse to scan micro-partitions or perform new calculations, they do not incur additional compute costs.

Engine-Level (Incurs Compute Cost):

Sorting a column (Option B) is fundamentally different. While Snowsight allows you to click a column header to sort, this action frequently triggers a re-query or a secondary processing step if the entire result set is not already fully cached in the browser's memory. When you use "column options" to perform operations like sorting, filtering, or grouping on large datasets, Snowflake often has to leverage the virtual warehouse to reorganize the data. In the context of the Snowflake Data Analyst exam, sorting is identified as a transformation that requires active compute resources because the engine must evaluate the entire dataset to determine the new order of records.

Furthermore, even if a small result set is cached, complex sorting across large volumes of data necessitates warehouse involvement to ensure accuracy and handle "spilling" to local or remote storage if the sort operation exceeds available memory. Therefore, while visual "masks" are free, structural data reorganization like sorting is a compute-intensive task.

To correctly predict the output of this query, a Data Analyst must understand how Snowflake handles semi-structured data (JSON), specifically regarding path notation, data type casting, and the display of VARIANT values in the Snowsight UI.

1. Variant vs. Casted Types (Display): When you access a value within a JSON object using colon notation (e.g., json_col:employee.phone[0]), the resulting value is of the VARIANT data type. In the Snowflake Snowsight results pane, VARIANT strings are displayed with double quotes (e.g., "+1 223-256-7330"). However, when you explicitly cast a variant to a string type using the double-colon syntax (e.g., ::varchar), Snowflake extracts the value as a native SQL string, which is displayed without quotes (e.g., +1 028-735-4521).

2. Handling Empty Strings vs. Out-of-Bounds: In the provided JSON exhibit, the phone array contains four elements at indices 0, 1, 2, and 3. The elements at indices 2 and 3 are empty strings (""). When these are queried, Snowflake returns the empty string variant, which appears as "" in the result set. The query also attempts to access phone[4]. Since the array only has four elements (max index 3), index 4 is out-of-bounds. In Snowflake, accessing a non-existent index in an array or a non-existent key in an object returns NULL.

Evaluating the Options based on the exhibit:

Option B correctly reflects all these behaviors: WORK_PHONE is in quotes (Variant), OFFICE_PHONE1 is not in quotes (Casted), OFFICE_PHONE2 and OFFICE_PHONE3 show the empty strings present in the JSON, and OFFICE_PHONE4 correctly shows null for the out-of-bounds access.

Option A is incorrect because it misses the quotes for the variant column and shows the out-of-bounds index as an empty string instead of null.

Option C is incorrect because it fails to account for the empty strings present at indices 2 and 3.

Option D incorrectly mixes the casting logic and display.

This question tests the "Schema-on-Read" proficiency required of a SnowPro Advanced: Data Analyst, specifically the ability to predict exactly how transformed semi-structured data will materialize for end-users.

In Snowsight, the modern web interface for Snowflake, System Filters are specialized keywords that provide out-of-the-box interactivity for dashboards and worksheets. These filters allow non-technical users to manipulate the timeframes of visualizations (e.g., switching from "Last 7 days" to "Last 12 months") without requiring an analyst to manually rewrite the underlying SQL code.

The most critical keyword for temporal filtering is :daterange. When a dashboard contains a date filter widget (as shown in the provided exhibit), the :daterange keyword acts as a dynamic placeholder for the range selected by the user. Unlike a standard variable that might represent a single date, :daterange is specifically designed to handle the start and end boundaries of a period. When injected into a WHERE clause, Snowflake automatically expands this keyword into the appropriate logic to filter records between those two points in time.

Evaluating the Options:

Options A and B are incorrect because they use hard-coded logic (-7 days). While these would return data for the last week, they are static. Changing the filter in the dashboard UI would have no effect on these queries, failing the requirement to "easily change the time period" via the filter selection.

Option C is incorrect because :date_filter is not a reserved system keyword in Snowsight. While an analyst could create a custom filter with that name, it would not automatically link to the standard system date widget shown in the image.

Option D is the 100% correct answer. Using WHERE = :daterange is the documented standard for linking a query to the Snowsight system date filter. This ensures that every tile in the dashboard remains synchronized with the user's global date selection, providing a cohesive and interactive reporting experience.