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

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.

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.

Working with nested semi-structured data requires a deep understanding of Snowflake's FLATTEN function and path notation. In the provided JSON exhibit, the data is hierarchical: the root contains a key named "events", which is an array containing multiple objects. Each object within that array contains the target field "f".

To transform this into a "consumable, tabular format" where each instance of "f" appears as a separate row, the Data Analyst must "explode" the array. Simply using path notation like data:events:f (Option A) will fail or return NULL because the engine cannot directly map a single path through an array structure to multiple scalar results without an explicit relational transformation.

The LATERAL FLATTEN function is the standard tool for this task. It takes a semi-structured column (or a specific path within it) as an input and returns a relational result set. By specifying input => data:events, the Analyst instructs Snowflake to look specifically at the array. For every element found in that array, FLATTEN generates a new row in the result set. The output of the flatten operation includes a column named VALUE, which represents the specific element (in this case, an object) currently being processed.

Evaluating the Options:

Option A and C are incorrect because they attempt to use direct pathing on an array. Without flattening, the query engine sees the array as a single block of data and cannot reach the "f" key inside individual array elements.

Option B is incorrect because it flattens the root data object. While this works for top-level keys, it wouldn't automatically iterate through the "events" array to expose the "f" values inside it; it would treat the entire array as a single value.

Option D is the 100% correct answer. It correctly targets the "events" array for flattening and then utilizes path notation (value:f) followed by the appropriate type casting (::number) to produce the final tabular output. This is a core competency for any SnowPro Advanced Data Analyst handling modern telemetry or event-based data.

Calculating a moving average (or rolling average) is a standard time-series analysis technique used to smooth out short-term fluctuations and highlight longer-term trends. In Snowflake, this is accomplished using Window Functions and the ROWS framing clause.

To calculate a 7-day moving average when you have one record per day, the "window" or "frame" must encompass exactly 7 rows. In SQL windowing syntax, the CURRENT ROW counts as one of those days. Therefore, to reach a total of 7, you need to look back at the 6 preceding rows ($6 + 1 = 7$).

Evaluating the Options:

Options A and B use the SUM() function. While the sum is part of an average, the question specifically asks for the average itself.

Option D is incorrect because 7 PRECEDING AND CURRENT ROW actually creates an 8-day window (the current day plus the seven days before it).

Option C is the 100% correct answer. It uses the AVG() aggregate function and correctly defines the frame as 6 PRECEDING AND CURRENT ROW, ensuring the calculation reflects exactly one week of data including the current day.

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.

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.

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.

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.