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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineer needs to optimize the performance of a series of complex transformations performed using Snowflake stored procedures. These procedures involve multiple table joins, aggregations, and data filtering operations. The current execution time is unacceptably long. Which of the following optimization strategies are most likely to provide the greatest performance improvements, considering both code-level optimizations and Snowflake's architecture? Select all that apply.

A) Implement data caching within the stored procedures using temporary tables to store intermediate results, reducing the need to repeatedly query the same data.
B) Refactor the stored procedures to leverage set-based operations (e.g., using SQL statements with joins and aggregations) whenever possible, allowing Snowflake to parallelize the execution across multiple nodes.
C) Use external functions written in languages like Python or Java for computationally intensive tasks that are not efficiently handled by SQL, taking advantage of Snowflake's support for external functions.
D) Rewrite the stored procedures to use procedural logic (e.g., loops and cursors) more extensively, as this allows for fine-grained control over the execution flow and can improve performance for certain operations.
E) Ensure the underlying tables are appropriately clustered and consider using materialized views for frequently accessed aggregations or pre-computed results to improve query performance.

2. You are using Snowpark to perform a complex join operation between two large tables: 'ORDERS (1 OOGB) and 'CUSTOMER (50GB). The join is performed on 'ORDERS.CUSTOMER ID = CUSTOMER.ID. The query is running slower than expected. You have already confirmed that the warehouse size is adequate. Which of the following strategies, applied in combination , would most likely improve the join performance within a Snowpark context?

A) Ensure both tables are clustered on the join keys CORDERS.CUSTOMER_ID' and 'CUSTOMER.ID').
B) Use 'session.add_import to add external JAR dependencies. This would enable use of external libraries and improve performance.
C) Increase the 'AUTO RESIZE' setting on the warehouse to automatically scale up the warehouse size when the load increases.
D) Analyze the query profile in Snowflake's web UI to identify the specific bottleneck (e.g., excessive data spilling, high CPU utilization) and address it directly.
E) Use Snowpark's 'hint function to force a broadcast join, assuming the 'CUSTOMER table can fit into memory on the worker nodes.

3. You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?

A) Cast input arguments and the result to 'FLOAT within the UDF.
B) Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.
C) Avoid large or small number and stick to the limited range of input values.
D) Convert the input numbers to strings within the JavaScript UDF before performing the calculation.
E) Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.

4. A large e-commerce company uses Snowflake to store website clickstream data in a table named 'WEB EVENTS'. This table is partitioned using the 'EVENT DATE column. The company needs to analyze user behavior across different devices. A common query involves joining 'WEB EVENTS' with a smaller 'USER DEVICES' table (containing user-to-device mappings) to determine the device type for each event. However, the performance of this join operation is poor, especially when filtering 'WEB EVENTS' by a specific date range. The 'USER DEVICES table is small enough to fit in memory. What is the most effective approach to optimize this query for performance?

A) Use a 'LATERAL FLATTEN' function to process the data in parallel.
B) Create a materialized view that pre-joins 'WEB_EVENTS' and 'USER_DEVICES' tables without filtering
C) Broadcast the 'USER DEVICES table to all compute nodes before performing the join. (Hint: Consider using 'BROADCAST hint)
D) Convert the 'WEB EVENTS' table to use a VARIANT data type and query with JSON path expressions.
E) Use a standard 'JOIN' operation between 'WEB_EVENTS' and USER_DEVICES' without any modifications.

5. You are tasked with building an ETL pipeline that ingests JSON logs from an external system via the Snowflake REST API. The external system authenticates using OAuth 2.0 client credentials flow. The logs are voluminous, and you want to optimize for cost and performance. Which of the following approaches are MOST suitable for securely and efficiently ingesting the data?

A) Create a Snowflake external function that handles the API call and OAuth authentication. Use a stream on the external stage pointing to the external system's storage to trigger data loading into the final table.
B) Use Snowflake's Snowpipe with REST API by configuring the external system to directly push the logs to an external stage and configure Snowpipe to automatically ingest it.
C) Implement a custom API gateway using a serverless function (e.g., AWS Lambda, Azure Function) to handle authentication and batch the JSON logs before sending them to the Snowflake REST API. Write the API output to a Snowflake stage, then use COPY INTO to load into a final table.
D) Use the Snowflake REST API directly from your ETL tool, handling OAuth token management in the ETL tool. Load data into a staging table, then use COPY INTO with a transformation to the final table.
E) Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.

Solutions: