Free and Premium Snowflake DAA-C01 Dumps Questions Answers

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.

Working with semi-structured data types like Arrays is a core competency for a Snowflake Data Analyst. In Snowflake, arrays are zero-indexed, meaning the first element is at position 0. Consequently, the index of the last element is always the total number of elements minus one ($Size - 1$).

To retrieve an element from a specific index, Snowflake provides the GET() function. This function takes the array column and the calculated index as arguments. When combined with ARRAY_SIZE(), which returns the total count of elements in the array, the formula ARRAY_SIZE(COL_B)-1 accurately targets the final index regardless of the array's length.

Evaluating the Options:

Option B is incorrect because using ARRAY_SIZE as the index directly (without subtracting 1) results in an "out-of-bounds" error or returns NULL, because the index equals the length (e.g., in an array of 3 items, the max index is 2).

Option C is incorrect. While some programming languages (like Python) allow negative indexing to start from the end, Snowflake SQL does not support this shorthand for arrays; it would simply return NULL.

Option D is incorrect because LAST_VALUE is an Analytic/Window function used to find the last row in a sorted result set, not the last element within a single array cell.

Option A is the 100% correct approach. It uses the standard, robust method for dynamic index calculation. This ensures that even if different rows have arrays of different lengths, the query will always successfully "pluck" the final item from each. This skill is vital for Data Transformation tasks, such as extracting the most recent status from a history array.

The provided image shows a specific section of a Snowflake Query Profile, which is a visual representation of the execution plan and the actual performance of a query. Analyzing this profile is a critical skill for a Data Analyst to identify performance bottlenecks.

1. Identifying the Exploding Join: The most striking evidence in this profile is the relationship between the input and output row counts of the Join [5] operator.

Input Row Counts: The join receives approximately 95.26k rows from one branch and 150k rows from the other.

Output Row Count: The join produces a staggering 571.6M rows.

When a join operation produces a significantly larger number of rows than the sum of its inputs, it is known as an exploding join (or a "Cartesian product-like" behavior). This typically occurs when the join condition is not restrictive enough or when there are many-to-many relationships with duplicate keys in both joining tables. In this specific case, joining on C1.C_NATIONKEY = C2.C_NATIONKEY has caused the row count to balloon from thousands to over half a billion.

2. Impact on Performance: Exploding joins consume excessive CPU and memory resources to process the massive intermediate result set. This often leads to secondary problems like spilling (Option A), where the data exceeds the virtual warehouse's memory and must be written to disk. However, the profile clearly identifies the join itself as the root cause.

Evaluating the Options:

Option A is a symptom, but the visual evidence of row expansion directly points to the join logic itself.

Option C (inefficient pruning) would be identified by a high percentage of partitions scanned in the Table Scan nodes, not by a row count explosion after a join.

Option D is irrelevant; while foreign keys can help the optimizer, their absence doesn't cause this specific visual profile on its own.

Option B is the 100% correct answer. The "exploded" arrow indicating 571.6M rows leaving a join fed by significantly smaller inputs is the textbook definition of an exploding join.

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.

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.

The SAMPLE clause (or TABLESAMPLE) is used in Snowflake to return a subset of rows from a table. When performing analysis on massive datasets, sampling allows for faster query execution and reduced credit consumption while still providing a statistically representative view of the data.

There are two primary methods of sampling in Snowflake: BERNOULLI (row-based) and BLOCK (partition-based). The query in this question uses BLOCK sampling, which selects a specific percentage of micro-partitions (blocks) rather than individual rows. This method is significantly faster for very large tables because it avoids the overhead of scanning every single row within a block; it either includes the entire block or skips it entirely.

Evaluating the Syntax:

Probability: The value inside the parentheses (0.012) represents the probability percentage for inclusion. Unlike some systems that might use decimals (where 1.0 = 100%), Snowflake treats this number as a direct percentage. Therefore, 0.012 is exactly 0.012%, not 1.2%.

Repeatable/Seed: The REPEATABLE clause (or SEED) followed by a number (99992) ensures that the sampling is deterministic. If the underlying data does not change, running this same query multiple times with the same seed will return the exact same "random" subset of blocks.

Evaluating the Options:

Options A and C are incorrect because they misinterpret the probability 0.012 as 1.2%.

Option D is incorrect because it mistakenly identifies the seed number 99992 as a target row count.

Option B is the 100% correct answer as it accurately identifies the sampling method (BLOCK), the correct percentage probability (0.012%), and the role of the seed (99992).

Snowsight, the modern web interface for Snowflake, introduces robust collaboration features through shared worksheets. Understanding the synchronization and versioning logic is critical for the Data Presentation and Data Visualization domain.

When a worksheet is shared with collaborators, Snowflake enables real-time collaboration (Option D). This means that as one user types or modifies SQL code, those changes are immediately visible to all other users who have the worksheet open. This functionality is similar to co-authoring in modern document editors, allowing data teams to debug or develop complex queries synchronously without the need for manual copy-pasting or constant screen sharing.

Furthermore, Snowflake manages the execution history of shared worksheets through a structured versioning system (Option C). If multiple users are interacting with the same worksheet, Snowflake does not overwrite a single "master" state during execution. Instead, every time a user executes the worksheet (or a portion of it), Snowflake creates a new version of the worksheet content associated with that specific run. This ensures that the code used to generate a particular result set is preserved and tied to that specific Query ID in the history.

Evaluating the Options:

Option A is incorrect because while users can see current edits, viewing a full, navigable history of "past versions" is not a standard feature in the same way it is in dedicated version control systems like Git.

Option B is incorrect as worksheet sharing is restricted to users within the same Snowflake account. Sharing across accounts requires the use of Data Shares or Private Listings.

Option E is incorrect because Snowflake preserves the history rather than overwriting it, allowing analysts to audit who ran what code and when.

Options C and D are the 100% correct answers. They define the synchronous and persistent nature of Snowsight collaboration, ensuring that teams can work together effectively while maintaining an immutable trail of executions.

This scenario requires a specific combination of Snowflake's extensibility features: the ability to run Python (pandas) code, the ability to return multiple rows for a single input (tabular output), and high performance through vectorization.

A User-Defined Table Function (UDTF) is the correct architectural choice when an input value needs to be expanded into a set of rows. While a standard UDF returns exactly one value per input row, a UDTF can return zero, one, or many rows, making it ideal for "parsing" or "splitting" tasks. By using a Vectorized UDTF (specifically utilizing the Python handler with pandas), the Analyst can process blocks of rows as pandas DataFrames or Series. This is significantly more efficient than processing one row at a time because it reduces the overhead of the Snowflake-to-Python execution engine.

Evaluating the Options:

Option A is incorrect because a standard UDF can only return a single scalar value per input row, making it impossible to "return multiple rows of parsed output."

Option C is incorrect because External Functions are used to call code hosted outside of Snowflake (like an AWS Lambda). While they can process data, they introduce network latency and are not the native way to run pandas code for internal joins.

Option D is incorrect because Stored Procedures are generally used for administrative tasks and procedural logic. While they can return tables, they cannot be directly "joined" with other tables in a standard SELECT statement in the same way a UDTF can.

Option B is the correct choice. It satisfies the requirement for pandas integration, provides the necessary multi-row output via the process and end_partition methods, and allows the analyst to use the LATERAL keyword to join the parsed results directly with existing tables in a single SQL statement.

In Snowflake, the performance of a metadata-based query (like MAX, MIN, or COUNT) is typically near-instantaneous because Snowflake maintains constant-time statistics in its Cloud Services layer. For a standard table, SELECT MAX(num) does not even require a virtual warehouse to be active; it simply reads the value from the table's metadata.

However, when a Row Access Policy (RAP) is applied to a table, the query's behavior changes fundamentally. A row access policy is a security feature that restricts which rows are visible to a user based on their role or other attributes. To enforce this policy, Snowflake can no longer rely on the high-level metadata of the entire table because it must first determine which specific rows the user is authorized to see. Consequently, the query engine must scan the individual micro-partitions and evaluate the policy logic for every row (or block of rows) to filter out unauthorized data before calculating the maximum value. This turns a "metadata-only" operation into a data-scanning operation, which requires a running warehouse and significantly more time.

Evaluating the Options:

Option A is incorrect because the prompt states the tables are "similarly-sized." Even if it were slightly larger, a metadata lookup for a standard table would still be nearly instant.

Option C is incorrect because a multi-cluster warehouse helps with concurrency (multiple users), not the raw execution speed of a single simple aggregate query.

Option D is incorrect because USE_CACHED_RESULT refers to the Query Result Cache. While turning it off would prevent a "0ms" response from a previous run, it wouldn't explain a "significant" slowdown compared to a standard metadata fetch.

Option B is the 100% correct answer. The presence of a Row Access Policy forces a full data scan and policy evaluation, which is the most common reason for performance degradation in otherwise simple metadata queries.

To determine the correct result of the query, a Data Analyst must evaluate the nested functions according to standard SQL order of operations while correctly identifying how Snowflake handles NULL values in aggregate calculations.

Step 1: Evaluating AVG(GRADE)

The AVG() function calculates the arithmetic mean of the non-null values in a column. Based on the provided table data, the grades are: 4, 1, 2, 1, and 2. The sixth row contains a NULL grade. In Snowflake, as per ANSI SQL standards, aggregate functions ignore NULL values entirely. They are not treated as zeros, nor do they cause the function to return NULL (unless the entire column is null).

Sum of non-null values: $4 + 1 + 2 + 1 + 2 = 10$

Count of non-null values: $5$

Result: $10 / 5 = 2.0$

Step 2: Evaluating CEIL(..., 2)

The CEIL (or CEILING) function is typically used to return the smallest integer value that is greater than or equal to the input. However, Snowflake's CEIL function also supports an optional second argument for scale (precision).

The input is the result from the previous step: 2.0.

The scale argument is 2.

The function CEIL(2.0, 2) attempts to round the value up to the nearest value with two decimal places. Since 2.0 is already a whole number and has no fractional component beyond the specified scale, the ceiling remains 2.0.

Evaluating the Options:

Option A is incorrect as it represents a massive scaling error.

Option C and D are incorrect because they result from mathematical errors or incorrectly including the NULL row in the denominator (e.g., $10 / 6 = 1.66$, then rounded up).

Option B is the 100% correct answer. It accurately reflects the result of the AVG function ($2.0$) followed by the CEIL operation, which preserves the value as it is already at the target ceiling. This question tests precision in both mathematical logic and the technical nuances of Snowflake-specific SQL functions.

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.

To determine the correct output, a Data Analyst must understand the behavior of the STRTOK_SPLIT_TO_TABLE table function and how it interacts with different delimiters.

1. Functionality of STRTOK_SPLIT_TO_TABLE:

The STRTOK_SPLIT_TO_TABLE function tokenizes a string based on a set of specified delimiters. Unlike simple SPLIT functions that often look for a literal string as a delimiter, STRTOK treats the delimiter string as a set of individual characters. Any character present in the delimiter string will trigger a split. Crucially, it skips empty tokens—if multiple delimiters appear consecutively, it does not produce a row for the space between them.

2. Evaluating the Data and Delimiters ('*-'):

'JOHN*DOE': The * character is a delimiter. This results in two tokens: JOHN and DOE.

'ALICE-SMITH': The - character is a delimiter. This results in two tokens: ALICE and SMITH.

'BOB*-JOHNSON': Both * and - are delimiters. Because they appear consecutively (*-), the function splits at each but ignores the empty space between them. This results in two tokens: BOB and JOHNSON.

3. Analyzing the Resulting Rows:

The LATERAL join produces a row for every token generated. The columns SEQ (identifying the source row sequence) and INDEX (position of the token within that sequence) are used for sorting.

Student 1: 2 tokens (JOHN, DOE) $\rightarrow$ 2 rows.

Student 2: 2 tokens (ALICE, SMITH) $\rightarrow$ 2 rows.

Student 3: 2 tokens (BOB, JOHNSON) $\rightarrow$ 2 rows.

Evaluating the Options (image_8233d8.png):

Option A incorrectly only shows results for Student 3.

Option C incorrectly implies Student 1 and 2 produce three tokens each.

Option D incorrectly interprets the consecutive delimiters in Student 3's name as creating a different sequence count.

Option B is the 100% correct result set. It accurately shows that each student’s name is split into exactly two parts, properly handling the varying delimiters and the consecutive characters in the third record.

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.

Manually defining table structures for complex semi-structured files like Parquet can be error-prone and time-consuming. Snowflake provides a specific automation workflow to handle this, involving the detection of the file's internal schema and the dynamic creation of a matching table.

The process starts with the INFER_SCHEMA function. Because Parquet files are self-describing, they contain metadata about their columns and data types. INFER_SCHEMA reads this metadata from files in a stage and returns a list of column names and types. To turn this list into an actual table, the analyst uses the CREATE TABLE ... USING TEMPLATE syntax. This command takes the output of INFER_SCHEMA as an input and automatically builds a table with the corresponding definition.

Evaluating the Options:

Option A is incorrect because CREATE TABLE LIKE is used to copy the structure of an existing table, not to build a new one from file metadata.

Option C and D are incorrect because GENERATE_COLUMN_DESCRIPTION is a helper function used to create a formatted string of column definitions, but it is not the primary command used with USING TEMPLATE for automated table creation.

Option B is the Correct answer. The combination of INFER_SCHEMA (to find the columns) and USING TEMPLATE (to build the table) is the standard Snowflake pattern for schema-on-read automation in Data Ingestion workflows.

To predict future values based on historical data, an analyst must determine the mathematical relationship between two variables—typically time (independent variable) and revenue (dependent variable). This is the foundation of linear regression.

The REGR_SLOPE function is a linear regression function that calculates the slope of the "least squares" regression line for non-null pairs in a group. In the context of sales forecasting, the "slope" represents the rate of change in revenue over time. By calculating the slope, an analyst can project that trend forward to estimate what the revenue will be at a future date (the end of the year).

Evaluating the Options:

Option A (CORR) measures the correlation coefficient, which tells you how strongly two variables are related (between -1 and 1), but it does not provide a mathematical formula to predict a specific future value.

Option C (COVAR_SAMP) calculates the sample covariance, which indicates the direction of a linear relationship but not the magnitude or slope required for prediction.

Option D (VARIANCE) is a descriptive statistic that measures data spread (how far numbers are from the mean) and is not used for trend projection or prediction.

Option B is the 100% correct answer. Along with REGR_INTERCEPT, REGR_SLOPE allows the analyst to build the linear equation $y = mx + b$ to perform predictive analytics.

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.

While Snowflake provides various ways to encapsulate logic, Stored Procedures are the primary tool for procedural programming and dynamic SQL execution. Unlike standard UDFs, which are generally designed to calculate and return values within a query, stored procedures can execute administrative commands (DDL) and data manipulation (DML) that are not possible in a simple SELECT statement.

Using Snowflake Scripting (or languages like Python, Java, or JavaScript), an analyst can write a procedure that builds a SQL string as a variable and then executes it using the EXECUTE IMMEDIATE command. This is demonstrated in procedural logic flows where inputs determine specific execution paths. While Tasks can be used to schedule the execution of these procedures, the task itself is a scheduling agent and does not provide the logic-driven dynamic string construction inherent to the procedure.

To achieve the desired result, an analyst must understand the fundamental behavior of different JOIN types within Snowflake and how they affect the retention of records from the "left" or primary table. The goal here is to list all brokers, even those who have zero customers (like "Drew") or customers with zero orders (like "Debby").

In SQL, an INNER JOIN only returns rows when there is a match in both tables. If we were to use an INNER JOIN between BROKER and CUSTOMER, Drew would be excluded from the results because he has no associated records in the CUSTOMER table. Similarly, an INNER JOIN with the ORDERS table would exclude any broker whose customers haven't placed an order.

Evaluating the Join Logic:

Option C is the correct solution because it utilizes a chain of LEFT JOINs. A LEFT JOIN (or LEFT OUTER JOIN) ensures that every record from the left table (BROKER) is preserved in the result set. If no matching record exists in the joined table (CUSTOMER or ORDERS), Snowflake populates the columns with NULL. This is why "Drew" appears with a CUST_COUNT of 0 and "Debby" appears with a NULL for the total order amount.

Option A fails because it uses an INNER JOIN for the CUSTOMER table, which would immediately filter out "Drew."

Option B and Option D fail because they use INNER JOINs at different stages of the query, which would strip away brokers or customers that do not have matching order activity.

Additionally, the query correctly uses COUNT(DISTINCT c.customer_id) to ensure that customers are not double-counted if they have multiple orders, and GROUP BY 1 (referencing b.broker_name) to aggregate the data at the broker level. This pattern is essential for accurate Data Analysis in Snowflake when dealing with "optional" relationships in a star or snowflake schema.