No help, full refund

Our company is committed to help all of our customers to pass Snowflake DEA-C02 as well as obtaining the IT certification successfully, but if you fail exam unfortunately, we will promise you full refund on condition that you show your failed report card to us. In the matter of fact, from the feedbacks of our customers the pass rate has reached 98% to 100%, so you really don't need to worry about that. Our DEA-C02 exam simulation: SnowPro Advanced: Data Engineer (DEA-C02) sell well in many countries and enjoy high reputation in the world market, so you have every reason to believe that our DEA-C02 study guide materials will help you a lot.

We believe that you can tell from our attitudes towards full refund that how confident we are about our products. Therefore, there will be no risk of your property for you to choose our DEA-C02 exam simulation: SnowPro Advanced: Data Engineer (DEA-C02), and our company will definitely guarantee your success as long as you practice all of the questions in our DEA-C02 study guide materials. Facts speak louder than words, our exam preparations are really worth of your attention, you might as well have a try.

After purchase, Instant Download: Upon successful payment, Our systems will automatically send the product you have purchased to your mailbox by email. (If not received within 12 hours, please contact us. Note: don't forget to check your spam.)

Under the situation of economic globalization, it is no denying that the competition among all kinds of industries have become increasingly intensified (DEA-C02 exam simulation: SnowPro Advanced: Data Engineer (DEA-C02)), especially the IT industry, there are more and more IT workers all over the world, and the professional knowledge of IT industry is changing with each passing day. Under the circumstances, it is really necessary for you to take part in the Snowflake DEA-C02 exam and try your best to get the IT certification, but there are only a few study materials for the IT exam, which makes the exam much harder for IT workers. Now, here comes the good news for you. Our company has committed to compile the DEA-C02 study guide materials for IT workers during the 10 years, and we have achieved a lot, we are happy to share our fruits with you in here.

Convenience for reading and printing

In our website, there are three versions of DEA-C02 exam simulation: SnowPro Advanced: Data Engineer (DEA-C02) for you to choose from namely, PDF Version, PC version and APP version, you can choose to download any one of DEA-C02 study guide materials as you like. Just as you know, the PDF version is convenient for you to read and print, since all of the useful study resources for IT exam are included in our SnowPro Advanced: Data Engineer (DEA-C02) exam preparation, we ensure that you can pass the IT exam and get the IT certification successfully with the help of our DEA-C02 practice questions.

Free demo before buying

We are so proud of high quality of our DEA-C02 exam simulation: SnowPro Advanced: Data Engineer (DEA-C02), and we would like to invite you to have a try, so please feel free to download the free demo in the website, we firmly believe that you will be attracted by the useful contents in our DEA-C02 study guide materials. There are all essences for the IT exam in our SnowPro Advanced: Data Engineer (DEA-C02) exam questions, which can definitely help you to passed the IT exam and get the IT certification easily.

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

1. Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

A) Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.
B) Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
C) Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.
D) Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION
E) Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.

2. Which of the following statements are TRUE regarding Snowflake's Fail-safe mechanism and its relation to Time Travel? (Select all that apply)

A) The Fail-safe period starts immediately after the Time Travel retention period ends.
B) Users can query data directly from Fail-safe using SQL commands if Time Travel is insufficient.
C) Fail-safe provides a historical data retention period of 7 days, similar to the default Time Travel setting.
D) Fail-safe is exclusively used by Snowflake to recover data in the event of a catastrophic system failure, and users have no direct access.
E) Fail-safe is automatically enabled for all Snowflake accounts and requires no configuration.

3. You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.

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

4. A data engineering team is using Snowflake's data lineage features, and they need to audit changes to data masking policies applied to a table named 'EMPLOYEES'. They want to identify when a masking policy was added, modified, or removed from specific columns.
What are the recommended Snowflake features or audit logs that the data engineering team could use to get these requirements?

A) Snowflake's native Data Lineage feature automatically captures all changes to data masking policies without any additional configuration, and those changes are then available to the data steward through the user interface.
B) Snowflake event tables provide complete audit trail capabilities. These tables capture all the events including policies.
C) The 'INFORMATION SCHEMA.POLICY REFERENCES view to determine what masking policies are currently in place. Then, combine that with the use of Snowflake's Alerting framework to get notified on the creation/removal of tables, and also on changes on the masking policies via SYSTEM$GET_PRIVILEGES() function.
D) The Account Usage view 'POLICY REFERENCES coupled with 'QUERY HISTORY, filtering for 'ALTER TABLE MODIFY COLUMN SET MASKING POLICY statements and also comparing snapshots of the 'POLICY_REFERENCES' view over time.
E) The 'OBJECT DEPENDENCIES' view in the ACCOUNT USAGE schema will directly track changes related to masking policies applied to tables since that is the best place for lineage information.

5. Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

A) Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
B) Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
C) Create a temporary table with average salaries per department, then join it back to the original DataFrame.
D) Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
E) Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.

Solutions: