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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 creating a resilient data pipeline using Snowpark Python. The pipeline transforms data from a raw stage to a processed stage. A key transformation involves joining two DataFrames, 'dfl' and 'df2 , based on a common column, 'id'. You want to ensure that even if 'df2 is temporarily unavailable or contains unexpected data, the pipeline continues to process 'dfl' using a default value for missing data from 'df2. Which of the following approaches provides the best balance of resilience and data integrity? Assume you have defined a default dataframe 'df default' already.

A. Use 'broadcast hint on 'df2 before performing the join to reduce the chances of join failure, assuming 'df2 is a small dataframe.
B. Use a 'try-except' block to catch any exceptions during the join operation. If an exception occurs, use the 'fillna()' method to replace missing values with the default data value.
C. Perform a 'left_outer' join of 'dfl' with 'df2. If 'df2 is unavailable or returns no data, replace 'df2' with a default DataFrame Cdf_default) and proceed with the join.
D. Perform a 'left_outer' join of 'dfl' with 'df2. If the join fails, catch the exception and proceed without the join.
E. Write a custom Python UDF that attempts to retrieve the corresponding data from 'df2 based on the 'id' column. If the retrieval fails for a particular ID, return a default value.

Question 2

You have an external table named in Snowflake that points to a set of CSV files in an AWS S3 bucket. The CSV files have a header row, and the data is comma-separated. However, some of the files in the S3 bucket are gzipped. You need to define the external table to correctly read both compressed and uncompressed files. Which of the following SQL statements BEST achieves this?

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

Question 3

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. 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.
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. 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
E. 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.

Question 4

A data engineering team has deployed an external function that leverages a cloud-based machine learning model. They are experiencing intermittent errors and performance degradation, and they suspect issues with the external function's batching and error handling. Which of the following steps would BEST address these issues and improve the reliability and performance of the external function? (Select TWO)

A. Implement robust error handling within the external function's code to catch exceptions and return meaningful error messages to Snowflake.
B. Increase the 'MAX BATCH_ROWS' parameter of the external function to send larger batches of data to the remote service.
C. Remove the "RETURNS NULL ON NULL INPUT clause from the external function definition to force all inputs to be processed.
D. Decrease the timeout period for the external function to quickly fail and retry in case of errors.
E. Monitor the external function's execution metrics using Snowflake's query history and the remote service's monitoring tools to identify bottlenecks and errors.

Question 5

A data engineer is facing performance issues with a complex analytical query in Snowflake. The query joins several large tables and uses multiple window functions. The query profile indicates that a significant amount of time is spent in the 'Remote Spill' stage. This means the data from one of the query stages is spilling to the remote disk. What are the possible root causes for 'Remote Spill' and what steps can be taken to mitigate this issue? Select two options.

A. The query is using a non-optimal join strategy. Review the query profile and consider using join hints to force a different join order or algorithm.
B. The virtual warehouse is not appropriately sized for the volume of data and complexity of the query. Increasing the virtual warehouse size might provide sufficient memory to avoid spilling.
C. The data being queried is stored in a non-Snowflake database, making it difficult to optimize the join.
D. The window functions are operating on large partitions of data, exceeding the available memory on the compute nodes. Try to reduce the partition size by pre- aggregating the data or using filtering before applying the window functions.
E. The 'Remote Spill' indicates network latency issues between compute nodes. There is nothing the data engineer can do to fix this; it is an infrastructure issue.

Solutions: