Snowflake DEA-C02 Exam : SnowPro Advanced: Data Engineer (DEA-C02)

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Snowflake DEA-C02 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. You have a Snowflake table 'raw_data' with columns 'id', 'timestamp', and 'payload'. A stream is defined on this table. A data pipeline reads changes from the stream and applies transformations before loading the data into a target table. However, the pipeline needs to handle cases where updates to the same 'id' occur multiple times within a short period, and only the latest version of the 'payload' should be processed. How can you achieve this idempotent processing of stream data to ensure only the latest payload is applied to the target table, avoiding duplicates and inconsistencies, using Snowflake streams?

A) Create a materialized view on the stream, grouping by 'id' and selecting the maximum 'timestamp' and corresponding 'payload'. Then, consume the materialized view instead of the stream.
B) Use a regular Snowflake task to periodically merge the stream data into the target table, overwriting any existing records with the same Sid'.
C) Before loading data into target table, create a temporary table by grouping Sid' and selecting the maximum 'timestamp' and corresponding 'payload' from stream. Finally, load this data into target table.
D) Configure the stream with a unique key constraint on the Sid' column to prevent multiple updates for the same Sid' from being captured.
E) When processing data from the stream, use a MERGE statement with a staging table. Load all stream changes into the staging table, then merge from the staging table to the target table using 'timestamp' to identify the latest version.

2. You have a Snowflake table, 'raw_data', which contains a column 'data url' storing URLs pointing to CSV files with varying schemas. Each CSV file represents sales data, but the column names and data types can differ. You need to create a process to automatically discover the schema of each CSV file, load the data into Snowflake, and standardize the column names to 'order id', 'product id', 'quantity', and 'price'. Which of the following approaches best addresses this requirement, considering scalability and minimal manual intervention?

A) Create a stored procedure that iterates through each URL in 'raw_data' , downloads the CSV file using 'SYSTEM$URL_GET , parses the CSV header to determine the column names, manually maps the discovered column names to the standardized names, creates a temporary table with the discovered schema, loads the data into the temporary table, transforms the data to use the standardized column names, and then inserts the transformed data into a final target table. Drop the temporary table after successful insertion.
B) Leverage a combination of Snowflake Scripting and External functions: create external function that infer the schema of the CSV, create temporary table based on identified schema, fetch the CSV data using SYSTEM$URL GET using snowflake scripting, copy the data into the temporary table, tranform the data into required structure, ingest into target table and finally drop the temporary table
C) Create a Python-based external function that downloads the CSV file from the URL using a library like 'pandas', infers the schema using 'pandas.read_csv' , maps the discovered column names to the standardized names, and returns the data as a JSON string. Then, create a Snowflake table with a VARIANT column, call the external function for each URL, and load the returned JSON data into the table. Create a view on top of it.
D) Create a Snowflake external table that points to the external stage. Define a single file format to be used by external table. Define a pipe that uses 'COPY INTO' to ingest data into external table from the files found at the file URLs.
E) Use Snowpipe with auto-ingest to continuously load the CSV files into a VARIANT column in a staging table. Create a series of views on top of the staging table, each view attempting to extract data based on different potential schema variations. Union all the views together to create a single consolidated view.

3. You are developing a data pipeline in Snowflake that uses SQL UDFs for data transformation. You need to define a UDF that calculates the Haversine distance between two geographical points (latitude and longitude). Performance is critical. Which of the following approaches would result in the most efficient UDF implementation, considering Snowflake's execution model?

A) Create a SQL UDF that directly calculates the Haversine distance using Snowflake's built-in mathematical functions (SIN, COS, ACOS, RADIANS). This is straightforward and easy to implement.
B) Create an External Function (using AWS Lambda or Azure Functions) to calculate the Haversine distance. This allows for offloading the computation to a separate compute environment.
C) Create a SQL UDF that pre-calculates the RADIANS for latitude and longitude only once and stores them in a temporary table, using those values for subsequent distance calculations within the same session.
D) Create a Java UDF that calculates the Haversine distance, leveraging optimized mathematical libraries. This allows for potentially faster execution due to lower- level optimizations.
E) Create a SQL UDF leveraging Snowflake's VECTORIZED keyword, hoping to automatically leverage SIMD instructions, without any code changes to mathematical calculation inside the UDF

4. You are implementing row access policies on a 'SALES DATA table to restrict access based on the 'REGION' column. Different users are allowed to see data only for specific regions. You have a mapping table 'USER REGION MAP' with columns 'USERNAME' and 'REGION'. You want to create a row access policy that dynamically filters the 'SALES DATA' based on the user and their allowed region. Which of the following options represents a correct approach to create and apply this row access policy?

A) Option E
B) Option D
C) Option C
D) Option A
E) Option B

5. A data engineer is tasked with implementing a data governance strategy in Snowflake. They need to automatically apply a tag 'PII CLASSIFICATION' to all columns containing Personally Identifiable Information (PII). Given the following requirements: 1. The tag must be applied as close to data ingestion as possible. 2. The tagging process should be automated and scalable. 3. The tag value should be dynamically set based on a regular expression match against column names and data types. Which of the following approaches would be MOST effective and efficient in achieving these goals?

A) Manually tag each column containing PII using the Snowflake web UI or the 'ALTER TABLE ... ALTER COLUMN ... SET TAG' command. Train data stewards to identify and tag new columns.
B) Implement a custom application using the Snowflake JDBC driver to periodically scan table schemas, detect PII columns, and apply tags using dynamic SQL.
C) Create a Snowflake Task that runs daily, querying the INFORMATION SCHEMCOLUMNS view, identifying potential PII columns based on regular expressions, and then executing ALTER TABLE ... ALTER COLUMN ... SET TAG commands.
D) Use Snowflake's Event Tables in conjunction with a stream and task. Configure the stream to capture DDL changes, and the task to evaluate new columns and apply the tag based on the column metadata using regular expressions.
E) Implement a stored procedure that leverages external functions to call a Python script hosted on AWS Lambda, which uses a machine learning model to identify PII and apply Snowflake tags.

Solutions: