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

1. A data engineer is tasked with optimizing query performance on a Snowflake table named 'SALES DATA, which currently has no clustering key defined. The table contains 'SALE (unique identifier), 'SALE DATE, 'PRODUCT CATEGORY, and 'SALE AMOUNT. The business analysts frequently run queries filtering on 'SALE DATE and then aggregating by 'PRODUCT CATEGORY'. Choosing the right clustering keys for the SALES DATA table is crucial for minimizing disk 1/0 and enhancing query speed. Which of the following clustering key strategies would be MOST effective for the specified query patterns, considering both performance and the potential impact on data loading and DML operations?

A) Clustering on followed by 'SALE_DATE'.
B) Creating separate tables for each 'PRODUCT CATEGORY.
C) Clustering only on 'SALE DATE
D) Clustering on 'SALE DATE followed by 'PRODUCT CATEGORY.
E) Clustering only on PRODUCT_CATEGORY.

2. A data engineer is tasked with creating a Listing to share a large dataset stored in Snowflake. The dataset contains sensitive Personally Identifiable Information (PII) that must be masked for certain consumer roles. The data engineer wants to use Snowflake's dynamic data masking policies within the Listing to achieve this. Which of the following approaches is the MOST secure and maintainable way to implement this requirement, assuming that the consumer roles are pre-defined and known?

A) Create a view that applies conditional masking using 'CASE' statements based on the function and share the view in the Listing.
B) Implement an external function that masks the data based on the consumer's role and share this function in the Listing. Use this external function in a view shared through the listing.
C) Apply dynamic data masking policies directly to the base tables containing the PII and share these tables in the Listing. Policies should use the function to determine when to mask the data.
D) Create multiple versions of the shared tables, each with different masking applied. The data engineer must manually manage which version each consumer can access.

3. You are tasked with optimizing a Snowpipe Streaming pipeline that ingests data from Kafka into a Snowflake table named 'ORDERS' You notice that while the Kafka topic has high throughput, the data ingestion into Snowflake is lagging. The pipe definition is as follows: "sql CREATE OR REPLACE PIPE ORDERS_PIPEAS COPY INTO ORDERS FROM @KAFKA STAGE FILE_FORMAT = (TYPE = JSON); Which of the following actions, taken individually, would be MOST effective in improving the ingestion rate, assuming sufficient compute resources are available in your Snowflake virtual warehouse?

A) Implement batching within the Kafka producer to send larger messages.
B) Increase the size of the virtual warehouse associated with the Snowflake account.
C) Increase the number of Kafka partitions and ensure Snowflake has enough compute to consume them in parallel using Snowpipe Streaming.
D) Enable auto-ingest for the Snowpipe.
E) Tune the parameter in the file format definition to a smaller value.

4. You are designing a Snowflake data pipeline that continuously ingests clickstream dat a. You need to monitor the pipeline for latency and throughput, and trigger notifications if these metrics fall outside acceptable ranges. Which of the following combinations of Snowflake features and techniques would be MOST effective for achieving this goal?

A) Use Snowflake's Event Tables and Event Notifications to capture events related to data ingestion and processing. Configure alerts based on event patterns that indicate latency or throughput issues.
B) Implement a combination of Snowflake Streams, Tasks, and external functions. Streams capture changes, Tasks process the changes, and external functions send notifications to a monitoring service when latency or throughput issues are detected.
C) Use Snowflake's 'QUERY_HISTORY view to track query execution times and implement a scheduled task that queries this view, calculates latency and throughput, and sends email notifications using Snowflake's built-in email integration if thresholds are exceeded.
D) Rely on Snowflake's default resource monitors to track warehouse usage. If warehouse usage exceeds a certain threshold, assume there are performance issues and send a notification.
E) Create a custom dashboard using a Bl tool that connects to Snowflake via JDBC/ODBC and visualizes data ingestion and processing metrics. Manually monitor the dashboard for anomalies.

5. You have a Snowflake table named 'ORDERS' with columns 'ORDER D', 'CUSTOMER D', and 'ORDER JSON' (a variant column storing order details). You need to extract specific product names from the 'ORDER JSON' column for each order and return them as a table. The 'ORDER JSON' structure is an array of objects, where each object represents a product with fields like 'product_name' and 'quantity'. Which approach is the most efficient and scalable way to achieve this, considering the possibility of millions of rows in the 'ORDERS table?

A) Create a Java UDF that takes ORDER JSON' as input, parses it using a JSON library, extracts the product names, and returns a comma-separated string. Use a WHILE loop within the UDF to parse the JSON array.
B) Create a Python UDTF that takes 'ORDER JSON' as input, parses it, and yields a row for each product name extracted. Use LATERAL FLATTEN within the UDTF for optimized JSON processing.
C) Create a JavaScript UDF that takes ' ORDER_JSON' as input and returns an array of product names. Use 'JSON.parse()' to parse the JSON string and iterate using array methods.
D) Create a SQL UDF that iterates through the JSON array using SQL commands and returns a comma-separated string of product names. Then, use SPLIT TO_TABLE to convert the string to rows.
E) Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.

Solutions: