Snowflake DEA-C02 pass test : 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. A data engineer is responsible for a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a data warehouse. The engineer needs to implement a notification system to alert them when specific data quality issues occur, such as data duplication exceeding a threshold or a sudden drop in data volume. Which approach offers the MOST flexible and scalable solution for implementing these notifications?

A) Use Snowflake's built-in resource monitors to track data volume and configure alerts based on predefined thresholds. This approach is simple but limited in its ability to detect complex data quality issues.
B) Create a series of Snowflake Tasks that execute SQL queries to check for data quality issues. If an issue is detected, the task triggers an external function to send a notification to a messaging service (e.g., AWS SNS, Azure Event Grid).
C) Implement a data quality monitoring tool that integrates with Snowflake via JDBC/ODBC and uses its own rules engine and notification system to detect and alert on data quality issues.
D) Develop custom SQL scripts to periodically query the data for quality issues and send email notifications using Snowflake's stored procedures and the 'EMAIL' external function.
E) Rely solely on Snowflake's Data Sharing feature to share the data with a data quality team who will manually review the data and report any issues.

2. You are tasked with implementing column-level security on the 'EMPLOYEE table to restrict access to the 'SALARY column. Only users with the 'HR ROLE' should be able to view the actual salary. All other users should see NULL. You create a masking policy as follows:

What additional steps are necessary to enforce this policy?

A) Grant the SELECT privilege on the EMPLOYEE table to the HR ROLE.
B) Grant OWNERSHIP on the masking policy to the HR ROL
C) Apply the masking policy to the EMPLOYEE table: ALTER TABLE EMPLOYEE SET MASKING POLICY salary_mask;
D) Grant the APPLY MASKING POLICY privilege to the HR_ROLE.
E) Apply the masking policy to the SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask;

3. 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 data being queried is stored in a non-Snowflake database, making it difficult to optimize the join.
B) 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.
C) 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.
D) 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.
E) 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.

4. A data engineer is tasked with processing a large dataset of customer orders using Snowpark Python. The dataset contains a column stored as a string in 'YYYY-MM-DD HH:MI:SS' format. They need to create a new DataFrame with only the orders placed in the month of January 2023. Which of the following code snippets achieves this most efficiently, considering potential data volume and query performance?

A)

B)

C)

D)

E)

5. You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?

A) Increase the size of the Snowflake warehouse used for the Snowpark session.
B) Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.
C) Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
D) Disable auto-tuning for the warehouse to ensure consistent performance
E) Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.

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