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

1. 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 single Small warehouse with auto-suspend set to 60 minutes.
B) A single X-Large warehouse with auto-suspend set to 5 minutes.
C) 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.
D) 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.
E) Three separate warehouses: a Medium warehouse for analytical queries, a Large warehouse for batch transformations, and an X-Small warehouse for ad-hoc queries.

2. Your team is implementing Snowpipe Streaming to ingest data from multiple IoT devices into a Snowflake table. Each device sends data continuously, and it's crucial to minimize latency. However, you observe that some records are occasionally missing in the target table. You have verified that the devices are sending all data and the client-side application using Snowpipe Streaming is functioning correctly. Which of the following is the MOST likely cause of the missing records, and how can you address it?

A) The Sequence Token is not being managed correctly, leading to potential gaps or out-of-order delivery; ensure correct implementation of the Sequence Token mechanism in your ingestion code.
B) Duplicate records are being dropped due to Snowflake's internal deduplication mechanism; configure the table to allow duplicates.
C) The Snowpipe Streaming client is exceeding its buffer capacity, leading to data loss; increase the buffer size on the client-side.
D) There is a delay in Snowflake's metadata refresh causing some new tables to be temporarily unavailable, retry after some time or manually refresh metadata.
E) The Snowflake virtual warehouse is not scaled up sufficiently to handle the ingestion rate; increase the virtual warehouse size.

3. You need to load data from a stream of CSV files into a Snowflake table. The CSV files are delivered to an AWS S3 bucket and contain header rows. The files occasionally include records where a text field contains a delimiter character (comma) within the text itself, but these fields are properly enclosed within double quotes. You want to create a file format object that correctly handles the data, including quoted delimiters, and skips the header row. Which of the following file format options are required to achieve this? (Choose two)

A) FILE_FORMAT = (TYPE = CSV)
B) ERROR ON COLUMN COUNT MISMATCH = FALSE
C) FIELD DELIMITER = ','
D) FIELD OPTIONALLY ENCLOSED BY =
E) SKIP HEADER = 1

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

5. You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

A) Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
B) Leverage Snowflake's event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
C) Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.
D) Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.
E) Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.

Solutions: