Snowflake DAA-C01 - SnowPro Advanced: Data Analyst Exam

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 the Snowflake data loading hierarchy, understanding the order of precedence for file format options is critical for troubleshooting and automation. Snowflake allows you to define file format options (such as CSV delimiter, header settings, or compression type) at multiple levels: as a standalone named File Format object, within a Stage definition, or directly inside a COPY INTO statement.

When data is loaded, Snowflake evaluates these definitions based on a specific override logic. The COPY INTO statement sits at the top of this hierarchy. This means that any file format options or named file formats explicitly called out in the COPY INTO command will override any conflicting settings defined at the stage or table level. For example, if a stage is defined to expect comma-separated files, but the COPY INTO command specifies a pipe delimiter, Snowflake will use the pipe delimiter for that specific load operation.

Evaluating the Options:

Options A, B, and C are incorrect because while they provide default behaviors for specific objects or namespaces, they are considered "lower-level" configurations.

Option D is the 100% correct answer. The COPY INTO statement provides the final, explicit instruction to the query engine. This design allows Data Analysts to use general-purpose stages for various file types by simply specifying the unique format requirements at the time of the execution, providing maximum flexibility in the Data Ingestion workflow.

The GENERATOR table function is used in Snowflake to produce a synthetic result set, typically for testing or creating dummy data. However, the GENERATOR function requires at least one of two parameters to be explicitly set to a positive value to produce any rows: ROWCOUNT or TIMELIMIT.

In the provided query, the user has set TIMELIMIT => NULL. In Snowflake's SQL implementation for the GENERATOR function, NULL is treated as a zero or an undefined limit. Furthermore, since the ROWCOUNT parameter is omitted, it defaults to zero. Because neither a positive duration nor a positive row count has been specified, the generator engine has no instruction on how many rows to build.

Evaluating the Results:

Option A is a distractor; while one might think NULL means "no limit" (infinity), the GENERATOR function is designed to be finite and safe.

Option B is incorrect; the number 12 inside the RANDOM(12) function is merely a seed value for the random number generator, ensuring the output is reproducible. It has no impact on the number of rows produced by the FROM clause.

Option C is incorrect; 10 is just a boundary for the UNIFORM function.

Option D is the 100% correct answer. Because the query specifies TIMELIMIT => NULL and fails to provide a ROWCOUNT, Snowflake will execute the query successfully but return a result set containing 0 rows. This is a common "trick" question in the Data Analysis domain to test an analyst's familiarity with table function parameters.

The INFER_SCHEMA function is a powerful tool in the Snowflake ingestion toolkit, specifically designed to bridge the gap between semi-structured data and relational tables. In modern data engineering, files like Parquet, Avro, and ORC are "self-describing," meaning they contain their own schema definitions (field names and data types) within the file header.

Before INFER_SCHEMA existed, a Data Analyst had to manually inspect a Parquet file, identify the keys, determine their data types, and then manually write a CREATE TABLE statement with the matching columns. INFER_SCHEMA automates this by scanning a set of files in a stage and returning a result set that describes the detected schema. This allows for the dynamic creation of tables that perfectly match the incoming data files.

Evaluating the Options:

Option A is incorrect because "unstructured data" (like PDFs or Images) does not have a schema that can be "inferred" in a relational sense.

Option C is incorrect because INFER_SCHEMA operates on files in a Stage, not on data already sitting in a table cell. To parse JSON in a table, you would use functions like FLATTEN or simple path notation.

Option D is incorrect because the DESCRIBE TABLE or GET_DDL commands are used to retrieve definitions for existing tables.

Option B is the Correct answer. It specifically identifies the role of the function: detecting metadata (schema) from staged semi-structured files. This function is often used in conjunction with GENERATE_COLUMN_DESCRIPTION and CREATE TABLE ... USING TEMPLATE to fully automate the schema evolution and ingestion process.

When dealing with massive datasets containing billions of rows, efficiency is paramount. Snowflake provides two primary ways to retrieve a small subset of data: Sampling and Result Set Limiting.

The SAMPLE clause is used to return a subset of rows based on a specific count or percentage. The correct syntax for specifying a fixed number of rows is SAMPLE ( ROWS). This is highly effective for exploratory data analysis where you need to see the shape of the data without the overhead of scanning the entire table. It is important to note that SAMPLE does not guarantee a truly random distribution unless specified, but it is much faster than a full table scan.

The TOP <n> or LIMIT <n> clauses are used to restrict the number of rows returned in the output. The TOP syntax is commonly used in many SQL dialects and is fully supported in Snowflake to return the first "n" rows the query engine encounters. While TOP is often used with an ORDER BY clause to get the highest or lowest values, using it alone on a massive table like FCT_SALES is a standard way to quickly peak at the data.

Evaluating the Options:

Option A is incorrect syntax; the ROWS keyword and parentheses are required for a fixed count.

Option B is Correct; it uses the standard Snowflake SAMPLE syntax for a specific row count.

Option C and Option E are incorrect because ROWS is not a valid keyword within a TOP clause in Snowflake.

Option D is Correct; it uses the standard TOP syntax to limit the result set.

By using these methods, a Data Analyst ensures they are not wasting compute resources (Credits) by pulling billions of unnecessary rows into the UI or a local environment.

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.

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.

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 Snowsight, when you create a chart, you define a bucket (the X-axis or a grouping dimension) and then select an aggregate function to determine the values displayed (the Y-axis). Snowsight provides a specific subset of standard SQL aggregate functions that are optimized for rapid, interactive visualization.

Any Value (Option A): This function (equivalent to ANY_VALUE) is often used when a dimension is functionally dependent on the bucket, and you simply need to pluck a representative value from the group.

Average (Option B): This is a fundamental aggregation for identifying the central tendency of a bucketed dataset.

Max (Option F): Along with SUM, MIN, and COUNT, MAX is a standard, built-in option for visualizing extremes within a data bucket.

Evaluating the Options:

Options C and D (Standard Deviation and Mode) are common statistical measures, but they are not available as direct, one-click options in the Snowsight chart aggregation menu; they would require custom SQL logic in the worksheet first.

Option E (HLL) is a probabilistic estimation function. While powerful in SQL, it is not a standard visualization aggregation in the UI.

Options A, B, and F are the 100% correct options. They represent the native, out-of-the-box functions available to a Data Analyst for summarizing data in Snowsight dashboards.