100% Snowflake DEA-C02 Guaranteed Success With Testing Engine

Snowflake DEA-C02 Exam Overview:

Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:

Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.

A) The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.
B) The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.
C) The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.
D) The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.
E) The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.

2. You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?

A) Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.
B) Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.
C) Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.
D) Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.
E) Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.

3. You're designing a Snowpark Scala stored procedure that must execute a series of complex data quality checks on a Snowflake table.
These checks involve multiple steps, including validating data types, checking for null values, and verifying data consistency against external reference data'. You want to ensure that the stored procedure is resilient to errors, provides detailed logging, and can be easily monitored. Which of the following approaches would be the MOST robust and scalable for handling errors and logging within this Snowpark Scala stored procedure?

A) Use Scala's 'Try' monad to handle exceptions, mapping successes to informational messages and failures to error messages. Log these messages using Snowflake's event tables.
B) Use Scala's 'Option' type to handle potential null values and exceptions. Return a string message indicating success or failure for each check. Log these messages using 'System.out.println'.
C) Rely on Snowflake's built-in error handling and logging mechanisms. If an error occurs, the stored procedure will automatically fail, and the error details can be retrieved from Snowflake's query history.
D) Implement a custom logging framework within the Scala stored procedure that writes detailed logs to a dedicated Snowflake table. Use try-catch blocks to handle exceptions and log error details, including timestamps, error codes, and relevant data values. Use Snowflake's 'SYSTEM$LAST QUERY ID()' function to track query lineage.
E) Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.

4. You have a table 'SALES DATA' in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data'. You know there were significant changes to the 'SALES DATA' table structure on '2024-01-15'. Your goal is to create a clone that only includes the structure as of '2024-01-14'. Which Snowflake command is MOST appropriate for this scenario?

A)

B)

C)

D)

E)

5. Your team is developing a set of complex analytical queries in Snowflake that involve multiple joins, window functions, and aggregations on a large table called 'TRANSACTIONS. These queries are used to generate daily reports. The query execution times are unacceptably high, and you need to optimize them using caching techniques. You have identified that the intermediate results of certain subqueries are repeatedly used across different reports, but they are not explicitly cached. Given the following options, which combination of strategies would MOST effectively utilize Snowflake's caching capabilities to optimize these analytical queries and improve report generation time?

A) Use temporary tables to store the intermediate results of the subqueries. These tables will be automatically cached by Snowflake and can be reused by subsequent queries within the same session.
B) Create common table expressions (CTEs) for the subqueries and reference them in the main query. CTEs will force Snowflake to cache the results of the subqueries, improving performance.
C) Utilize the "RESULT_SCAN' function in conjunction with the query ID of the initial subquery execution to explicitly cache and reuse the results in subsequent queries. This approach requires careful management of query IDs.
D) Create materialized views that pre-compute the intermediate results of the subqueries. This will allow Snowflake to automatically refresh the materialized views when the underlying data changes and serve the results directly from the cache.
E) Consider using 'CACHE RESULT for particularly expensive subqueries or views. This is a hint to snowflake to prioritize caching the result set for future calls.

Solutions: