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

1. 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 '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.
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) 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.
E) 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.

2. You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A) Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
B) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
C) Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
D) Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
E) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.

3. A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

4. A data engineering team is managing a Snowflake warehouse that supports a high volume of ad-hoc queries from data analysts exploring a large, semi-structured JSON dataset containing website clickstream data'. The query performance is frequently slow, and analysts are complaining about long wait times. The warehouse is already sized appropriately. You have identified that many of the queries filter on nested JSON attributes that are not explicitly indexed. Considering only query acceleration service features, what is the MOST effective approach to improve query performance for these ad-hoc queries without modifying the queries themselves or significantly increasing storage costs?

A) Enable the Materialized View feature to create materialized views over the frequently queried nested JSON attributes.
B) Use a combination of materialized views and query acceleration for best performance.
C) Enable Automatic Clustering on the table to improve data organization based on query patterns.
D) Create a dedicated virtual warehouse specifically for ad-hoc queries, and enable query acceleration on this warehouse.
E) Enable Search Optimization Service for the table containing the JSON data. Selectively enable search optimization on the specific columns and nested paths that are frequently used in WHERE clause predicates.

5. You're tasked with building an external function in Snowflake that calls an API to enrich customer data with geographical information (latitude and longitude) based on their IP address. The API endpoint requires an API key passed in the headen Your external function definition looks like this: "'sql CREATE OR REPLACE EXTERNAL FUNCTION VARCHAR) RETURNS VARIANT VOLATILE MAX BATCH ROWS = 100 RETURNS NULL ON NULL INPUT API INTEGRATION = AS 'https://api.example.com/geo'; Which of the following steps are essential to ensure the external function correctly passes the API key to the external service, handles rate limiting from the API, and correctly parses the JSON response from the external service (Assume the API returns a JSON object with 'latitude' and 'longitude' fields)?

A) Ensure the Snowflake user executing the function has the 'USAGE' privilege on the API integration object.
B) Modify the external function code within the external service (e.g., AWS Lambda or Azure Function) to read the API key from an environment variable, construct the appropriate request headers (including the API key), implement exponential backoff with jitter for handling rate limits based on retry-after header if received, and ensure proper error handling for JSON parsing failures and return Snowflake compatible results.
C) Create a Snowflake API integration object that includes the 'ALLOWED_HOSTS parameter pointing to 'https://api.example.com', and configure the service to return retry-after header on rate limit and ensure retryPolicy is set to 3 retries.
D) Modify the 'enrich_ip' function to directly embed the API key within the API endpoint URL as a query parameter.
E) Implement robust logging within the external service (e.g., AWS Lambda or Azure Function) to capture details of requests, responses, and any errors encountered. Monitor these logs regularly to identify and address potential issues.

Solutions: