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Snowflake SnowPro Advanced DEA-C02 SnowPro Advanced: Data Engineer (DEA-C02) Exams
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Snowflake DEA-C02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Security and Governance | 15% | - Data Security
|
| Data Transformation with Snowflake | 30% | - Data Processing Patterns
|
| Performance Optimization | 15% | - Data Optimization
|
| Data Architecture and Processing | 20% | - Data Storage Architecture
|
| Data Ingestion and Consumption | 20% | - Data Unloading
|
Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:
Question #1
You are using the Snowflake REST API to insert data into a table named 'RAW JSON DATA. The JSON data is complex and nested, and you want to efficiently parse and flatten it into a relational structure. You have the following JSON sample:
Which SQL statement, executed after loading the raw JSON using the REST API, is the MOST efficient way to flatten the JSON and extract relevant fields into a new table named 'PURCHASES' with columns like 'EVENT TYPE', 'USER D', 'EMAIL', 'STREET, 'CITY', 'ITEM ID', and 'PRICE'?
A. Option D
B. Option C
C. Option E
D. Option A
E. Option B
Question #2
You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.
A. Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.
B. Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.
C. Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.
D. Restore the entire Snowflake account to a point in time before the incorrect transformation.
E. Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
Question #3
You have a table 'EVENTS' containing application event data with columns 'EVENT ID, 'USER ID, 'EVENT TYPE, and EVENT DETAILS (VARCHAR). The 'EVENT DETAILS column contains comma-separated key-value pairs (e.g., 'location=USA,device=mobile,os=iOS'). Your objective is to transform this structured data into a VARIANT column named EVENT JSON' in a new table 'EVENTS JSON'. The data in EVENT DETAILS has inconsistent key-value pairs across different rows. Which of the following methods are the most efficient and scalable to parse the key-value pairs in 'EVENT DETAILS' and construct the JSON objects?
A. Use only REGEXP EXTRACT ALL' with appropriate regular expressions to extract all keys and values into arrays, then use a JavaScript UDF to combine them into a JSON object.
B. Use a combination of 'SPLIT, 'REGEXP_REPLACE and 'OBJECT_CONSTRUCT within a user-defined function (UDF) to parse the string and build the JSON object.
C. Use a Java UDF that iterates through the string, splitting it based on commas and equals signs, and then constructs a JSON object using a JSON library.
D. Use 'SPLIT to split the key-value pairs into an array, then use a LATERAL FLATTEN to create rows from array, then use 'SPLIT again to split each row by '='. Finally, construct the JSON using 'OBJECT CONSTRUCT.
E. Utilize to split the key-value pairs into rows, then use 'REGEXP_EXTRACT to extract the key and value. Finally, use 'OBJECT_CONSTRUCT and to construct the JSON object.
Question #4
A data engineer is using Snowpark Scala to create a UDF that calculates the distance between two geographical coordinates (latitude and longitude) using the Haversine formula'. The function should accept four 'Double' values (latl, lonl , lat2, lon2) and return the distance in kilometers as a 'Double'. The UDF must be named 'haversine distance'. What is the correct Scala code to define and register this UDF with Snowflake, including the import statements required for using Snowpark functions?
A. Option D
B. Option C
C. Option E
D. Option A
E. Option B
Question #5
You are developing a JavaScript stored procedure in Snowflake using Snowpark to transform data'. The procedure needs to efficiently calculate the sum of 'amount' for each 'customer_id' in a large table named 'orders'. You want to avoid transferring large amounts of data to the client and utilize Snowpark's pushdown capabilities. Which of the following JavaScript code snippets is the MOST efficient and correct way to achieve this? Assume 'snowflake' binding is available.
A. 
B. 
C. 
D. 
E. 
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: A,B | Question #3 Correct Answer: D,E | Question #4 Correct Answer: E | Question #5 Correct Answer: E |
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