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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are planning to monetize a dataset on the Snowflake Marketplace. You want to provide potential customers with sample data to evaluate before they purchase a full subscription. Which of the following strategies are valid and recommended for offering a free sample of your data within the Snowflake Marketplace? (Select all that apply)

A) Create a separate share containing a subset (e.g., a smaller number of rows or columns) of the full dataset and offer this share as a free trial listing on the Marketplace.
B) Provide the consumer with the script to create a database link to your data, allowing them read-only access to a pre-defined sample table, and then revoke the access after a set period.
C) Upload a sample CSV file to a publicly accessible S3 bucket and provide the link in the Marketplace listing description. Consumers can download and load this data into their own Snowflake account for evaluation.
D) Offer a 'free trial' subscription on the primary listing that automatically expires after a set period (e.g., 7 days), allowing customers to access the full dataset during the trial period. You will need to write custom code to manage trial expiration and data access restrictions based on the trial status.
E) Create a view that filters the dataset based on a sampling algorithm (e.g., 'SAMPLE ROW' clause) and share the view through the Marketplace.

2. 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 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
D) 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.
E) 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.

3. You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?

A) The masking policy syntax is incorrect. It should use 'CASE WHEN IS_ROLE_IN_SESSION('DATA_ADMIN') THEN EMAIL ELSE '[email protected]' END'.
B) The masking policy is applied to the wrong column. It should be applied to the ID column, not the EMAIL column.
C) The WITH clause is unneccessary.
D) Without masking poliy code, it's impossible to determine if there is anything wrong.
E) There is no code provided, so there is nothing wrong with it.

4. A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

A) Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
B) Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.
C) Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
D) Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.
E) Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.

5. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

Solutions: