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

1. You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
B) Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
C) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
D) Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
E) Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.

2. You are using the Snowflake Developer API to automate the creation and management of masking policies. You need to create a masking policy that masks an email address using SHA256 hashing. You also want to ensure that the policy can be applied to multiple tables and columns without modification. Assuming you have already established a connection to Snowflake using the Developer API, which of the following code snippets BEST demonstrates how to create and apply this masking policy using Python?

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

3. You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

A) Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
B) Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
C) Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.
D) Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
E) Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.

4. 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) Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.
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) 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.

5. You have a Snowflake task that executes a complex stored procedure. This stored procedure performs several UPDATE statements on a large table. After enabling the 'QUERY TAG' parameter, you notice that the task history in Snowflake shows frequent suspensions due to exceeding warehouse resource limits. The warehouse is already scaled to the largest size. Which combination of the following strategies would BEST address this issue and minimize task suspensions, assuming you CANNOT further scale the warehouse?

A) Optimize the UPDATE statements in the stored procedure to reduce resource consumption by using techniques such as clustering keys, partitioning and avoiding full table scans.
B) Set a lower 'SUSPEND TASK AFTER N FAILURES' value to proactively suspend the task before it consumes excessive resources.
C) Break down the stored procedure into smaller, more manageable transactions and commit changes more frequently. Consider utilizing batch processing techniques.
D) Increase the 'ERROR ON N parameter for the task to allow for more consecutive timeouts before the task is suspended.
E) Implement a retry mechanism within the task's SQL code to automatically retry failed UPDATE statements after a short delay.

Solutions: