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

1. You are developing a JavaScript UDF in Snowflake to perform complex data validation on incoming data'. The UDF needs to validate multiple fields against different criteria, including checking for null values, data type validation, and range checks. Furthermore, you need to return a JSON object containing the validation results for each field, indicating whether each field is valid or not and providing an error message if invalid. Which approach is the MOST efficient and maintainable way to structure your JavaScript UDF to achieve this?

A) Define a JavaScript object containing validation rules and corresponding validation functions. Iterate through the object and apply the rules to the input data, collecting the validation results in a JSON object. This object is returned as a string.
B) Directly embed SQL queries within the JavaScript UDF to perform data validation checks using Snowflake's built-in functions. Return a JSON string containing the validation results.
C) Create separate JavaScript functions for each validation check (e.g., 'isNull', 'isValidType', 'isWithinRange'). Call these functions from the main UDF and aggregate the results into a JSON object.
D) Utilize a JavaScript library like Lodash or Underscore.js within the UDF to perform data manipulation and validation. Return a JSON string containing the validation results.
E) Use a single, monolithic JavaScript function with nested if-else statements to handle all validation logic. Return a JSON string containing the validation results.

2. You are tasked with building a data pipeline that ingests JSON data from a series of publically accessible URLs. These URLs are provided as a list within a Snowflake table 'metadata_table', containing columns 'file_name' and 'file url'. Each JSON file contains information about products. You need to create a view that extracts product name, price, and a flag indicating whether the product description contains the word 'discount'. Which of the following approaches correctly implements this, optimizing for both performance and minimal code duplication, using external functions for text processing?

A) Create a stored procedure that iterates through 'metadata_table', downloads each JSON file using 'SYSTEM$URL GET, parses the JSON, extracts the required fields, and inserts the data into a target table. Then, create a view on top of the target table. Use 'LIKE '%discount%' to identify if a product description contains the word 'discount'.
B) Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$URL_GET' fetch the content from 'file_url'. The JSON can then be parsed and the fields like price, name and description can be fetched. Use within the view to flag the presence of discount.
C) Create an external function that takes a URL as input and returns a BOOLEAN indicating if any error occured while processing the URL and the data. Create a stored procedure that iterates through 'metadata_table' , calls external function for each URL, reports error and then processes the data. A stage must also be created to host external function code.
D) Create an external function that takes a URL as input and returns a JSON variant containing the extracted product name, price, and discount flag (using 'LIKE Then, create a view that selects from calls the external function with 'SYSTEM$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.
E) Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.

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 leveraging Snowflake's VECTORIZED keyword, hoping to automatically leverage SIMD instructions, without any code changes to mathematical calculation inside the UDF
B) 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.
C) Create a Java UDF that calculates the Haversine distance, leveraging optimized mathematical libraries. This allows for potentially faster execution due to lower- level optimizations.
D) 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.
E) 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.

4. A data provider wants to share a large dataset (several TB) with multiple consumers. The dataset is updated daily. The provider wants to minimize the cost associated with data sharing and ensure that consumers receive consistent data'. Which of the following strategies would be the MOST cost-effective and maintainable?

A) Create a data share with views that point to the base tables. Use time travel to allow consumers to query data from a specific point in time before the daily update.
B) Create a data share and create a separate database for each consumer, cloning the data into each consumer's database daily.
C) Create a data share and grant access to all consumers directly on the base tables.
D) Create a data share with views that point to the base tables, and clone the base tables daily into a separate 'staging' database before sharing.
E) Create a data share containing external tables pointing to data stored in cloud storage (e.g., AWS S3), updated daily using a pipeline.

5. You have a table 'SALES DATA' in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data'. You know there were significant changes to the 'SALES DATA' table structure on '2024-01-15'. Your goal is to create a clone that only includes the structure as of '2024-01-14'. Which Snowflake command is MOST appropriate for this scenario?

A)

B)

C)

D)

E)

Solutions: