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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table named 'ORDERS clustered on 'ORDER DATE. After a significant data load, you want to evaluate the effectiveness of the clustering. Which of the following SQL queries, using Snowflake system functions, will provide insights into the clustering depth and overlap of micro-partitions in the 'ORDERS' table, specifically helping you identify whether re-clustering is necessary? Assume that the table

A)

B)

C)

D)

E)

2. You are building a data pipeline using Snowflake Tasks to orchestrate a series of transformations. One of the tasks, 'task _ transform data', depends on the successful completion of another task, 'task extract_data'. However, occasionally fails due to transient network issues. You want to implement a retry mechanism for 'task_extract data' without impacting the overall pipeline execution time significantly. Which of the following approaches is the most appropriate and efficient way to achieve this within the Snowflake Task framework?

A) Implement a TRY...CATCH block within the task definition to catch any exceptions. Inside the CATCH block, use SYSTEM$WAIT to pause for a few seconds, then re- execute the core logic of the task. Repeat this process a limited number of times before failing the task permanently.
B) Modify the task definition to call a stored procedure. The stored procedure implements a loop with a retry counter. Inside the loop, execute the data extraction logic. If an error occurs, catch the exception, wait for a few seconds, and retry the extraction. After a specified number of retries, raise an exception to signal task failure.
C) Configure the task with an error notification integration that sends alerts upon failure. Manually monitor these alerts and manually resume the task if it fails. Use 'ALTER TASK task extract data RESUME;'
D) Create a new root-level task that checks the status of 'task_extract_data'. If it failed, the root-level task will execute a copy of the 'task_extract data' task. After this, it updates the 'task_transform_data"s 'AFTER' condition to depend on the new task that retries extraction.
E) Use the 'AFTER keyword in the 'CREATE TASK' statement for 'task_transform_data' to only execute if succeeds on its first attempt. If fails, the entire pipeline will stop, ensuring data consistency.

3. You are tasked with managing a large Snowflake table called 'TRANSACTIONS'. Due to compliance requirements, you need to archive data older than one year to long-term storage (AWS S3) while ensuring the queries against the current 'TRANSACTIONS' table remain performant. What is the MOST efficient strategy using Snowflake features and considering minimal impact on query performance?

A) Export the historical data to S3 using COPY INTO, truncate the 'TRANSACTIONS' table, and then create an external table pointing to the archived data in S3.
B) Partition the 'TRANSACTIONS table by date. Export the old partitions of the 'TRANSACTIONS' table to S3 using COPY INTO. Then, drop the old partitions from the 'TRANSACTIONS table and create an external table that points to the data in S3.
C) Create an external table pointing to S3. Then create new table named 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data from 'TRANSACTIONS' table into 'TRANSACTIONS ARCHIVE, and then delete the archived data from the 'TRANSACTIONS' table.
D) Use Time Travel to clone the "TRANSACTIONS' table to a point in time one year ago. Then, export the cloned table to S3 and drop the cloned table. Delete the archived data from the 'TRANSACTIONS table.
E) Create a new table 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data, and then delete the archived data from the 'TRANSACTIONS table.

4. 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 Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
C) Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
D) Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
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.

5. You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

A) Option B
B) Option D
C) Option C
D) Option A
E) Option E

Solutions: