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

1. You are tasked with building a data pipeline to process image metadata stored in JSON format from a series of URLs. The JSON structure contains fields such as 'image_url', 'resolution', 'camera_model', and 'location' (latitude and longitude). Your goal is to create a Snowflake table that stores this metadata along with a thumbnail of each image. Given the constraints that you want to avoid downloading and storing the images directly in Snowflake, and that Snowflake's native functions for image processing are limited, which of the following approaches would be most efficient and scalable?

A) Create a Python-based external function that fetches the JSON metadata and image from their respective URLs. The external function uses libraries like PIL (Pillow) to generate a thumbnail of the image and returns the metadata along with the thumbnail's Base64 encoded string within a JSON object.
B) Create a Snowflake stored procedure that iterates through each URL, downloads the JSON metadata using 'SYSTEM$URL_GET, extracts the image URL from the metadata, downloads the image using 'SYSTEM$URL_GET , generates a thumbnail using SQL scalar functions, and stores the metadata and thumbnail in a Snowflake table.
C) Create a Snowflake view that selects from a table containing the metadata URLs, using 'SYSTEM$URL GET to fetch the metadata. For each image URL found in the metadata, use a JavaScript UDF to generate a thumbnail. Embed the thumbnail into a VARCHAR column as a Base64 encoded string.
D) Store just the 'image_url' in snowflake. Develop a separate application using any programming language to pre generate the thumbnails and host those at publicly accessible URLs. Within Snowflake, create a view to generate the links for image and thumbnail using 'CONCAT.
E) Create a Snowflake external table that points to an external stage which holds the JSON metadata files. Develop a spark process to fetch image URL, create thumbnails and store as base64 encoded strings in an external stage, create a view using the external table and generated thumbnails data

2. 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) 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.
B) 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.
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.

3. 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) Configure the stream with a unique key constraint on the Sid' column to prevent multiple updates for the same Sid' from being captured.
B) 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.
C) 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.
D) Use a regular Snowflake task to periodically merge the stream data into the target table, overwriting any existing records with the same Sid'.
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.

4. A Snowflake data pipeline utilizes Snowpipe to ingest JSON data from cloud storage into a raw staging table 'RAW DATA' Subsequently, a series of transformation tasks are executed to cleanse, transform, and load the data into fact and dimension tables. You've noticed significant performance degradation in the transformation tasks, especially when dealing with large JSON payloads and deeply nested structures. Which of the following optimization techniques, applied at different stages of the pipeline, would MOST likely improve the overall performance of the data transformation tasks?

A) Using the file format option when defining the Snowpipe integration to remove the outer array from the JSON data before ingestion.
B) Partitioning the 'RAW DATA' staging table based on the ingestion timestamp to reduce the amount of data scanned during transformation.
C) Employing Snowflake's 'LATERAL FLATTEN' function with appropriate 'PATH' expressions to efficiently extract the required attributes from the JSON data during transformation.
D) Replacing the transformation tasks with external functions implemented in Python using Snowpark, leveraging the power of Pandas DataFrames for JSON processing.
E) Increasing the virtual warehouse size used by the transformation tasks to provide more compute resources.

5. You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

A) External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.
B) Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
C) All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
D) Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
E) Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.

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