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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 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. 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.
C. Create a Snowflake view that selects from a table containing the metadata URLs, using 'SYSTEM$URL GET to fetch the metadata. For each image URL found in the metadata, use a JavaScript UDF to generate a thumbnail. Embed the thumbnail into a VARCHAR column as a Base64 encoded string.
D. Create a Snowflake stored procedure that iterates through each URL, downloads the JSON metadata using 'SYSTEM$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.
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

Question 2

A data engineer is using Snowpark Scala to create a UDF that calculates the distance between two geographical coordinates (latitude and longitude) using the Haversine formula'. The function should accept four 'Double' values (latl, lonl , lat2, lon2) and return the distance in kilometers as a 'Double'. The UDF must be named 'haversine distance'. What is the correct Scala code to define and register this UDF with Snowflake, including the import statements required for using Snowpark functions?

A. Option B
B. Option A
C. Option D
D. Option C
E. Option E

Question 3

You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

A. Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
B. Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.
C. Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
D. Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.
E. Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.

Question 4

You are designing a data governance strategy for a Snowflake data warehouse. One of the key requirements is to track data lineage for sensitive data, specifically Personally Identifiable Information (PII). You need to understand how PII data flows through various transformations and tables. Which Snowflake feature, when combined with appropriate tagging and metadata management practices, can BEST help you achieve this?

A. Snowflake's Resource Monitors
B. Snowflake INFORMATION_SCHEMA views and Account Usage views
C. Snowflake Secure Data Sharing
D. Snowflake Cloning
E. Snowflake Data Masking policies

Question 5

You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

A.

B.

C.

D.

E.

Solutions: