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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 1

You have a Snowflake table named 'ORDERS' with columns 'ORDER D', 'CUSTOMER D', and 'ORDER JSON' (a variant column storing order details). You need to extract specific product names from the 'ORDER JSON' column for each order and return them as a table. The 'ORDER JSON' structure is an array of objects, where each object represents a product with fields like 'product_name' and 'quantity'. Which approach is the most efficient and scalable way to achieve this, considering the possibility of millions of rows in the 'ORDERS table?

A. Create a JavaScript UDF that takes ' ORDER_JSON' as input and returns an array of product names. Use 'JSON.parse()' to parse the JSON string and iterate using array methods.
B. Create a SQL UDF that iterates through the JSON array using SQL commands and returns a comma-separated string of product names. Then, use SPLIT TO_TABLE to convert the string to rows.
C. Create a Java UDF that takes ORDER JSON' as input, parses it using a JSON library, extracts the product names, and returns a comma-separated string. Use a WHILE loop within the UDF to parse the JSON array.
D. Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.
E. Create a Python UDTF that takes 'ORDER JSON' as input, parses it, and yields a row for each product name extracted. Use LATERAL FLATTEN within the UDTF for optimized JSON processing.

Question 2

A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

A. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
B. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
C. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
D. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
E. Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.

Question 3

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

Question 4

You are designing a data warehouse for an e-commerce company. One of the requirements is to provide fast analytics on order fulfillment times by region. You have two tables: 'ORDERS: Contains order information, including ID, 'ORDER DATE, 'REGION ID, and 'FULFILLMENT DATE. 'REGIONS': Contains region information, including 'REGION ID' and Due to the large size of the 'ORDERS' table and the complexity of calculating fulfillment times, you decide to use materialized views.
Which of the following combinations of materialized view definition and Snowflake features would BEST optimize query performance and minimize data staleness for this scenario? Choose two options.

A. Create a materialized view that joins 'ORDERS and 'REGIONS', calculates 'FULFILLMENT TIME', and groups by 'REGION NAME'. Do not specify a clustering key.
B. create a materialized view that joins 'ORDERS' and 'REGIONS', calculates 'FULFILLMENT_TIME' grouped by 'REGION_NAME, and cluster by 'REGION NAM Configure incremental data refreshes.
C. Use Snowflake's search optimization service on the 'ORDERS' table instead of creating a materialized view.
D. Create a materialized view that joins 'ORDERS and 'REGIONS', calculates the difference between 'FULFILLMENT DATE' and 'ORDER DATE as , and groups by REGION_NAME. Cluster the view by ' REGION_NAME.
E. Partition the 'ORDERS' table by 'ORDER_DATE and create a materialized view that calculates 'FULFILLMENT_TIME grouped by REGION_NAME , clustering by 'ORDER DATE'

Question 5

You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?

A. Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.
B. Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.
C. Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
D. Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.
E. Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.

Solutions: