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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are developing a data pipeline in Snowflake that processes sensitive customer data'. You need to implement robust data governance controls, including column-level security and data masking. Which of the following combinations of Snowflake features, when used together, provides the MOST comprehensive solution for achieving this?

A) Object tagging, column-level security policies (using views), and masking policies.
B) Row-level security policies and data masking policies.
C) Data masking policies and network policies.
D) Dynamic tables and masking policies.
E) Row access policies and data masking policies on base tables, supplemented with object tagging and column-level security policies on views that grant limited access to specific user roles.

2. Consider a scenario where you have a large dataset of sensor readings stored in a Snowflake table called 'SENSOR DATA'. You need to build an external function to perform complex calculations on these readings using a custom Python library hosted on AWS Lambda'. The calculation requires significant computational resources, and you want to optimize the data transfer between Snowflake and the Lambda function. The following SQL is provided: CREATE OR REPLACE EXTERNAL FUNCTION ARRAY) RETURNS ARRAY VOLATILE MAX BATCH ROWS = 2000 RETURNS NULL ON NULL INPUT API INTEGRATION = aws_lambda_integration AS 'arn:aws:lambda:us-east-1:123456789012:function:sensorProcessor'; Which of the following options would further optimize the performance and reduce data transfer costs, assuming the underlying Lambda function is correctly configured and functional?

A) Rewrite the custom Python library in Java and create a Snowflake User-Defined Function (UDF) instead of using an external function.
B) Compress the data before sending it to the external function and decompress it within the Lambda function. Update the Lambda function to compress the array of results before sending it back to Snowflake and use Snowflake+s functions to decompress it.
C) Convert the input data to a binary format (e.g., using 'TO_BINARY and FROM_BINARY' functions in Snowflake) before sending it to the Lambda function, and decode it in Lambda to reduce the size of the data being transmitted.
D) Increase the 'MAX BATCH ROWS' parameter to the maximum allowed value to send larger batches of data to the external function. Ensure Lambda function memory is increased appropriately.
E) Reduce the number of columns passed to the external function by performing pre-aggregation or filtering on the data within Snowflake before calling the function.

3. You are tasked with creating a system to monitor the data quality of a 'SALES DATA" table. The table is updated daily with new sales records, and you need to ensure that the 'SALE AMOUNT column always contains positive values. You decide to use Snowflake tasks and streams for this purpose. Consider the following Snowflake script. What is the most appropriate way to modify the 'SALES DATA' table so the task has a 'WHEN' clause that runs only if the 'SALE AMOUNT has negative values? Assume the stream 'SALES DATA STREAM' is properly configured on 'SALES DATA'.

A)

B)

C)

D)

E)

4. You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

A) Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.
B) Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.
C) Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.
D) Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.
E) Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.

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: