Snowflake DEA-C02 Exam Questions : SnowPro Advanced: Data Engineer (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 observes that a daily data transformation pipeline in Snowflake, which processes data from external stage 's3://my- bucket/raw_dataP , is consistently taking longer to complete. Upon investigation, the engineer finds that the COPY INTO statement is the bottleneck. The COPY INTO statement is as follows:

Which of the following could be the root cause of the performance degradation and how would you address them? Select two options.

A) The 'ON_ERROR = 'CONTINUE" option is causing the COPY INTO statement to perform additional error handling, slowing down the process. Remove the 'ON ERROR clause to improve performance.
B) The external stage contains a large number of small files. Snowflake's COPY INTO statement performs best with fewer, larger files. Consolidate the small files into larger files before loading.
C) The PATTERN '. .csv' is inefficient. Refine the PATTERN to be more specific, targeting only the necessary files, potentially using date-based partitioning. If date partitioning is used, leverage partition pruning by including the appropriate date criteria in the COPY INTO statement.
D) The virtual warehouse used for the COPY INTO operation is undersized. Increase the virtual warehouse size to improve performance.
E) Snowflake automatically optimizes COPY INTO operations. No specific action is needed.

2. You're loading data into a Snowflake table using 'COPY INTO'. You notice that some rows are being rejected due to data validation errors (e.g., data type mismatch, uniqueness constraint violations). You want to implement a strategy to capture these rejected rows for further analysis and correction. Which of the following approaches offers the MOST efficient and reliable method for capturing and storing the rejected rows, minimizing performance impact during the data loading process? Assume no staging table exists before loading data to production table.

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

3. You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?

A) Snowflake does not support Avro format for Snowpipe.
B) The data files in cloud storage are not being automatically detected by Snowpipe.
C) The file format definition in Snowflake is incompatible with the Avro schema.
D) The cloud storage event notifications are not properly configured to trigger Snowpipe.
E) The Snowpipe is paused due to exceeding the daily quota.

4. Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?

A) The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.
B) The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
C) The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
D) The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.
E) The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.

5. You are tasked with calculating the daily moving average of sales for each product category in your Snowflake data warehouse using Snowpark Python. You need to handle cases where there might be missing sales data for certain days. You have the following sales data available in a table named with columns: (DATE), (VARCHAR), and 'sales_amount' (NUMBER). Which of the following Snowpark Python code snippets correctly calculates the 7-day moving average of sales amount per product category, filling in missing sales days with 0, and handles potential division by zero?

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

Solutions: