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

1. You're building a data pipeline that ingests JSON data from URLs representing real-time weather information. The data structure varies slightly between different weather providers, but all contain a 'location' object with 'city' and 'country' fields, and a 'temperature' field. You need to create a generic function that can handle these variations and extract the location and temperature, returning a flattened JSON object with keys 'city', 'country', and 'temperature'. You want to avoid explicit schema definition and take advantage of Snowflake's VARIANT data type flexibility Given the following sample JSON structures, which approach will best accomplish this?

A) Define a Snowflake stored procedure that uses 'SYSTEM$URL_GET to fetch the JSON data, then uses conditional logic with 'TRY TO BOOLEANS and STRY TO DATE to handle different data types. The stored procedure constructs a new JSON object with 'city', 'country', and 'temperature' fields using 'OBJECT_CONSTRUCT.
B) Define a Snowflake external function (UDF) that fetches the JSON data using a Python library like 'requests' or The function then parses the JSON and extracts the required fields, handling potential missing fields using 'try...except' blocks. The function returns a JSON string representing the flattened object.
C) Create a Snowflake external function written in Java that uses 'java.net.lJRL' to fetch the JSON data and 'com.fasterxml.jackson.databind' library to parse it. Use Jackson's 'JsonNode' to navigate the varying JSON structure and extract 'city', 'country', and 'temperature' fields. Return a JSON string of the result.
D) Define a Snowflake view that selects from a table containing the URLs, using 'SYSTEM$URL GET to fetch the JSON data and to extract the 'city', 'country', and 'temperature' fields. Use 'TRY_CAST to convert the 'temperature' to a numeric type.
E) Create a pipe that uses 'COPY INTO to ingest JSON data directly from the URLs into a VARIANT column. The 'FILE FORMAT object is configured to use = TRUE to handle different data types. Post ingestion create a view to query data.

2. You are developing a data pipeline that uses Snowpipe Streaming to ingest JSON data into a Snowflake table. Some JSON documents contain nested arrays and complex structures. You need to flatten the JSON structure during ingestion to simplify querying. Consider the following JSON document: { "order id": 123, "customer": { "id": "cust123", "name": "John Doe", "address": { "street": "123 Main St", "city": "Anytown" } }, "items": [ {"product_id": "prodl", "quantity": 2}, {"product_id": "prod2", "quantity": 1} ] } Which approach would you use within the 'COPY INTO' statement of your Snowpipe to flatten this JSON structure during ingestion?

A) Snowpipe and the 'COPY INTO' command automatically flattens JSON data during ingestion; no additional steps are required.
B) Create a separate transformation pipeline using Snowflake Tasks to flatten the data after it is ingested into the table.
C) Pre-process the JSON documents before loading them into the stage using a custom script to flatten the structure.
D) Use JavaScript UDFs within the 'COPY INTO' statement to recursively flatten the JSON structure.
E) Use the ' FLATTEN()' table function directly within the 'COPY INTO' statement to expand the 'items' array and extract nested fields. For nested objects, use dot notation directly in the SELECT list (e.g., 'customer.name').

3. A financial institution is using Snowflake to store transaction data for millions of customers. The data is stored in a table named 'TRANSACTIONS with columns such as 'TRANSACTION ID, 'CUSTOMER ID', 'TRANSACTION DATE, 'TRANSACTION_AMOUNT, and 'MERCHANT CATEGORY'. Analysts are running complex analytical queries that often involve filtering transactions by 'TRANSACTION_DATE, 'MERCHANT CATEGORY' , and 'TRANSACTION_AMOUNT ranges. These queries are experiencing performance bottlenecks. The data team wants to leverage query acceleration service to improve performance without significantly altering the existing query patterns. Which of the following actions or combination of actions would be MOST beneficial, considering the constraints and the nature of the queries? (Select TWO)

A) Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.
B) Enable Automatic Clustering on the 'TRANSACTIONS' table, ordering the keys as 'TRANSACTION_DATE, 'MERCHANT_CATEGORY', 'CUSTOMER_ID. Then, enable query acceleration on the virtual warehouse.
C) Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.
D) Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
E) Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.

4. You are tasked with setting up a Kafka Connector to ingest data into Snowflake. You need to ensure fault tolerance. Which of the following Kafka Connect configurations are essential for enabling fault tolerance and ensuring minimal data loss during connector failures? Select all that apply.

A) Configure 'errors.tolerance' to 'all'.
B) Enable Kafka Connect's internal offset storage by configuring 'offset.storage.topic' and 'config.storage.topic'.
C) Set 'tasks.max' to a value greater than 1.
D) Configure 'errors.deadletterqueue.topic.name' to specify a Dead Letter Queue (DLQ) topic.
E) Utilize Snowflake's auto-ingest feature alongside the Kafka Connector.

5. You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?

A) Create a Snowflake alert that triggers when the difference in the total 'SALE_AMOUNT between the 'TRANSACTIONS' table and 'MONTHLY REPORTS' exceeds a predefined threshold within a specified time window.
B) Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.
C) Implement a Snowflake task that periodically recalculates the 'MONTHLY_REPORTS' table from the 'TRANSACTIONS table and compares the results with the existing data, logging any discrepancies. Use a smaller warehouse size to minimize cost.
D) Implement data validation checks within the data pipeline (streams and tasks) that update the 'TRANSACTIONS' table to reject transactions that violate predefined business rules.
E) Utilize Snowflake's Time Travel feature to compare the ' TRANSACTIONS' table and 'MONTHLY _ REPORTS' table at a specific point in time and identify the changes that led to the discrepancies.

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