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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 1

You've created a JavaScript stored procedure using Snowpark to transform data'. The stored procedure is failing, and you suspect an issue with how Snowpark is handling null values during a join operation. Given two Snowpark DataFrames, and 'df2 , what is the expected behavior when performing an inner join on a column containing null values in both DataFrames, and how can you mitigate potential issues?

A. The inner join will exclude rows where the join column is null in either DataFrame. To include these rows, you must use a full outer join instead.
B. Inner Join will not throw an error, and will exclude the rows where join column is null. If you need to join records with null values, pre-processing dataframes using to replace null with a valid sentinel value before performing the join is one way to handle this.
C. The inner join will treat null values as equal, resulting in rows where the join column is null in both DataFrames being included in the result. To avoid this, you should filter out null values before the join.
D. The behavior of the inner join with null values is undefined and may vary depending on the data types and the specific version of Snowpark. Explicit null handling is always required.
E. The inner join will automatically exclude rows where the join column is null in either DataFrame. There is no need for explicit null handling.

Question 2

You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

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

Question 3

You're using Snowpipe Streaming to ingest JSON data into a Snowflake table. The JSON data contains nested objects and arrays. You're encountering errors related to data type mismatches during ingestion. The target table schema is defined with specific data types for each column. Which of the following approaches is MOST effective for handling this data type mismatch issue within the Snowpipe Streaming context, considering minimal transformation?

A. Create a Stored Procedure in Snowflake to transform the data after it has been ingested using Snowpipe Streaming.
B. Use a COPY INTO statement with a transformation function to cast the data during ingestion.
C. Adjust the auto_ingest property of the Snowpipe object to force data type conversion.
D. Use a variant column in the Snowflake table and perform data type casting during querying.
E. Modify the client-side application to cast the data to the correct data type before sending it to Snowpipe Streaming.

Question 4

You have a Snowflake stage named that points to an S3 bucket containing JSON log files. You want to create a directory table to track the files and their metadata'. The log files are continuously being added to the bucket. Which of the following sets of steps is the MOST efficient way to create and maintain an up-to-date directory table for this stage?

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

Question 5

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 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.
B. 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
C. 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.
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 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.

Solutions: