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

1. 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 file format definition is missing a 'NULL IF' parameter which is causing Snowflake to attempt to load string values that should be NULL.
C) 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.
D) Snowpipe is encountering rate limiting issues with Azure Blob Storage. Implement retry logic in your Snowpipe configuration.
E) 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.

2. Given the following scenario: You have an external table 'EXT SALES in Snowflake pointing to a data lake in Azure Blob Storage. The storage account network rules are configured to only allow specific IP addresses and virtual network subnets, enhancing security. You are getting intermittent errors when querying 'EXT SALES. Which of the following could be the cause(s) and the corresponding solution(s)? Select all that apply.

A) The file format specified in the external table definition does not match the actual format of the files in Azure Blob Storage. Solution: Update the 'FILE_FORMAT parameter in the external table definition to match the correct file format.
B) The table function cache is stale, causing access to non-existent files. Solution: Run 'ALTER EXTERNAL TABLE EXT_SALES REFRESH'.
C) The network connectivity between Snowflake and Azure Blob Storage is unstable. Solution: Implement retry logic in your queries to handle transient network errors.
D) The Snowflake IP addresses used to access the Azure Blob Storage are not whitelisted in the storage account's firewall settings. Solution: Obtain the Snowflake IP address ranges for your region and add them to the storage account's allowed IP addresses.
E) The Snowflake service principal does not have the correct permissions on the Azure Blob Storage account. Solution: Ensure the Snowflake service principal has the 'Storage Blob Data Reader' role assigned to it.

3.

A) The Snowflake external function is not correctly parsing the JSON response from the Lambda function. Implement a wrapper function in Snowflake to parse the JSON and extract the discount value before returning it.
B) The Lambda function returns the discount within a nested JSON structure Tdata': [[discount]]}'. The Snowflake function is not designed to handle this. The lambda function should return '{'data':
C) The data types in the Lambda function and Snowflake function definition do not match. Specifically, the Lambda function expects strings while Snowflake is sending numbers and vice versa. Modify the Lambda function to handle numeric inputs and ensure the Snowflake function definition aligns with the expected output data type (FLOAT).
D) The 'RETURNS NULL ON NULL INPUT clause in the external function definition is causing the function to return NULL even when valid inputs are provided. Remove this clause.
E) The Lambda function is returning a string instead of a number. Modify the Lambda function to return the discount as a number (e.g., 'discount = 0.15' instead of 'discount = '0.15")

4. You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

A) Prompt the user for the PostgreSQL username and password each time the script is executed.
B) Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.
C) Hardcode the PostgreSQL username and password directly into the Python script.
D) Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
E) Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.

5. You are implementing a data pipeline to load data from AWS S3 into Snowflake. The source data consists of CSV files with a header row. Some of the CSV files have inconsistent data types in a specific column (e.g., sometimes an integer, sometimes a string). You want to use the 'COPY' command to load the data and handle these data type inconsistencies gracefully. Which of the following 'COPY' command options, used in conjunction, would BEST address this issue and avoid load failures? Assume the file format is already defined to specify CSV type, header skip, and field delimiter.

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

Solutions: