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

1. A financial institution needs to implement both dynamic data masking and column-level security on the 'CUSTOMER DATA table, which contains sensitive information like 'CREDIT CARD NUMBER and 'SSN'. The requirement is: all users except those in the 'DATA ADMIN' role should see masked credit card numbers (last 4 digits unmasked) and masked SSNs. Users in 'DATA ADMIN' should see the original data'. Which of the following combination of policies and grants will achieve this?

A) Create two masking policies: one for 'CREDIT CARD NUMBER and another for 'SSN' using the 'CASE' statement to apply masking logic based on the current_role(). Apply masking policies to the appropriate columns. Grant SELECT privilege on the table to PUBLIC.
B) Create two masking policies: one for 'CREDIT CARD NUMBER' and another for 'SSN'. Grant APPLY MASKING POLICY privilege to the 'DATA ADMIN' role. Do not grant any SELECT privileges on the table.
C) Create two masking policies: one for 'CREDIT CARD NUMBER and another for 'SSN'. Apply masking policies to the appropriate columns. Create a custom role with the 'APPLY MASKING POLICY privilege, grant this custom role to the 'DATA_ADMIN' role. Grant SELECT privilege on the table to PUBLIC.
D) Create two masking policies: one for 'CREDIT CARD NUMBER' and another for 'SSN'. Grant APPLY MASKING POLICY privilege to the 'DATA ADMIN' role. Apply masking policies to the appropriate columns. Grant SELECT privilege on the table to PUBLI
E) Create two masking policies: one for 'CREDIT CARD NUMBER' and another for 'SSN'. Grant APPLY MASKING POLICY privilege to the 'DATA ADMIN' role and then apply masking policies to the appropriate columns. Grant SELECT privilege on the table to PUBLIC.

2. A data engineer is using Snowpark Python to build a data pipeline. They need to define a UDF that uses a pre-trained machine learning model stored as a file in a Snowflake stage. The UDF should receive batches of data for scoring. Which of the following is the MOST efficient way to implement this, minimizing data transfer and execution time?

A) Load the model from the stage into a DataFrame, then use 'df.mapPartitionS to apply the model to each partition.
B) Create a UDF with gudf(packages=['snowflake-snowpark-python', 'scikit-learn'], input_types=[ArrayType(StringType())], return_type=FloatType(), replace=True, is_permanent=True, and load the model within the UDF's initialization using 'session.file.get' .
C) Create a UDF that reads the model from the stage for each row that is passed to it using 'session.file.get' inside the UDF's execution logic.
D) Use 'session.read.parquet' to load the model file directly into a Snowpark DataFrame and then use 'DataFrame.foreach' to process each row.
E) Use '@vectorized' decorator from Snowpark to process each batch of data passed to the UDF and load the model inside it. Specify the appropriate data types in the decorator.

3. You are using Snowpipe to continuously load JSON data from an Azure Blob Storage container into a Snowflake table. The data contains nested JSON structures. You observe that some records are not being loaded into the table, and the 'VALIDATION MODE shows 'PARSE ERROR' for these records. Examine the following COPY INTO statement and the relevant error message from 'VALIDATION MODE', and identify the most likely cause of the problem. COPY INTO my_table FROM FILE FORMAT = (TYPE = JSON STRIP OUTER ARRAY = TRUE) ON ERROR = CONTINUE; Error Message (from VALIDATION MODE): 'JSON document is not well formed: invalid character at position 12345'

A) The 'STRIP OUTER ARRAY' parameter is causing the issue because the incoming JSON data is not wrapped in an array. Remove the 'STRIP OUTER ARRAY' parameter from the COPY INTO statement.
B) The Snowflake table schema does not match the structure of the JSON data. Verify that the column names and data types in the table are compatible with the JSON fields.
C) Snowpipe is encountering rate limiting issues with Azure Blob Storage. Implement retry logic in your Snowpipe configuration.
D) The JSON data contains invalid characters or formatting errors at position 12345, as indicated in the error message. Cleanse the source data to ensure it is well-formed JSON before loading.
E) The file format definition is missing a 'NULL IF' parameter which is causing Snowflake to attempt to load string values that should be NULL.

4. A Snowflake Data Engineer is tasked with identifying all downstream dependencies of a view named 'CUSTOMER SUMMARY. This view is used by multiple dashboards and reports. They want to use SQL to efficiently find all tables and views that directly depend on 'CUSTOMER SUMMARY. Which of the following SQL queries against the ACCOUNT USAGE schema is the MOST efficient and accurate way to achieve this?

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

5. You are designing a data pipeline to load JSON data from an AWS S3 bucket into a Snowflake table. The JSON files have varying schemas, and you want to use schema evolution to handle changes. You are using a named external stage with 'AUTO REFRESH = TRUE. You notice that some files are not being ingested, and the COPY HISTORY shows 'Invalid JSON' errors. Which of the following actions would BEST address this issue while minimizing manual intervention?

A) Re-create the stage with the 'AUTO REFRESH = FALSE parameter and manually refresh the stage metadata after each file is uploaded. This gives more control over which files are processed.
B) Adjust the file format definition associated with the stage to be more permissive, allowing for variations in the JSON structure. For example, use 'STRIP OUTER ARRAY = TRUE and configure error handling within the file format.
C) Create a separate landing stage for potentially invalid JSON files and use a task to validate the files before moving them to the main stage for ingestion into Snowflake.
D) Implement a pre-processing step using a Snowpark Python UDF to cleanse the JSON files in the stage before the COPY INTO command is executed. This UDF should handle schema variations and correct any invalid JSON structures.
E) Modify the COPY INTO statement to include 'ON ERROR = SKIP FILE' to ignore files with invalid JSON and continue loading other files. This ensures the pipeline continues without interruption.

Solutions: