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

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

2. You are building a data pipeline in Snowflake that uses an external function to perform sentiment analysis on customer reviews stored in a table named 'CUSTOMER REVIEWS'. The external function 'sentiment_analyzer' is hosted on AWS Lambda and requires an API key for authentication. You want to ensure that the API key is securely passed to the Lambda function and prevent unauthorized access. Which of the following approaches represents the MOST secure and recommended method to manage the API key?

A) Store the API key in a Snowflake table with restricted access and retrieve it within the external function's logic.
B) Create a Snowflake secret object to store the API key and reference it in the external function definition using the 'USING' clause and 'SYSTEM$GET SECRET function.
C) Store the API key directly in the external function definition as a string literal within the 'AS' clause.
D) Embed the API key directly into the AWS Lambda function's environment variables, avoiding any transmission from Snowflake.
E) Pass the API key as a parameter to the external function each time it is called.

3. A data engineer is investigating high credit consumption on a Snowflake warehouse due to frequent re-clustering operations on a large table named 'WEB EVENTS. This table is clustered on 'EVENT TIMESTAMP' and 'USER ID. The engineer suspects that the high frequency of data ingestion, especially out-of-order 'EVENT TIMESTAMP' values, contributes to the poor clustering. Choose the options that can lead to optimizing clustering and reducing credit consumption, assuming you have limited control over the ingestion process and data quality.

A) Implement a pre-processing stage to sort the incoming data by 'EVENT TIMESTAMP before loading it into the 'WEB EVENTS table, using a temporary table and then inserting into the final table.
B) Drop the clustering key altogether to avoid re-clustering costs.
C) Increase the warehouse size to accelerate the re-clustering process.
D) Partition the table based on "EVENT _ TIMESTAMP' instead of clustering.
E) Implement a maintenance task to periodically re-cluster the table less frequently, but at more strategically chosen times (e.g., during off-peak hours).

4. You have a large Snowflake table 'WEB EVENTS that stores website event data'. This table is clustered on the 'EVENT TIMESTAMP column. You've noticed that certain queries filtering on a specific 'USER ID' are slow, even though 'EVENT TIMESTAMP clustering should be helping. You decide to investigate further Which of the following actions would be MOST effective in diagnosing whether the clustering on 'EVENT TIMESTAMP is actually benefiting these slow queries?

A) Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.
B) Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.
C) Query the 'QUERY_HISTORY view to see the execution time of the slow query and compare it to the average execution time of similar queries without a 'USER filter.
D) Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.
E) Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.

5. You are tasked with optimizing the performance of a Snowflake virtual warehouse used for running several types of queries: short- running analytical queries with strict latency requirements, long-running batch data transformations, and ad-hoc queries from data scientists. The workload is unpredictable, and the team wants to minimize queueing and maximize resource utilization. Which warehouse configuration would be MOST appropriate to handle this mixed workload, minimizing cost and maximizing performance?

A) A multi-cluster warehouse with a scaling policy of 'Standard' and a minimum of 1 and maximum of 3 clusters with auto-suspend set to 10 minutes.
B) Three separate warehouses: a Medium warehouse for analytical queries, a Large warehouse for batch transformations, and an X-Small warehouse for ad-hoc queries.
C) A single X-Large warehouse with auto-suspend set to 5 minutes.
D) A multi-cluster warehouse with a scaling policy of 'Economy' and a minimum of 1 and maximum of 2 clusters with auto-suspend set to 5 minutes.
E) A single Small warehouse with auto-suspend set to 60 minutes.

Solutions: