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

1. You have an external table in Snowflake pointing to data in Azure Blob Storage. The data consists of customer transactions, and new files are added to the Blob Storage daily You want to ensure that Snowflake automatically picks up these new files and reflects them in the external table without manual intervention. However, you are observing delays in Snowflake detecting the new files. What are the potential reasons for this delay and how can you troubleshoot them? (Choose two)

A) The storage integration associated with the external table does not have sufficient permissions to access the Blob Storage.
B) The Azure Event Grid notification integration is not properly configured to notify Snowflake about new file arrivals in the Blob Storage.
C) The file format used for the external table is incompatible with the data files in Blob Storage.
D) The external table's 'AUTO_REFRESH' parameter is set to 'FALSE', which disables automatic metadata refresh.
E) Snowflake's internal cache is not properly configured; increasing the cache size will solve the problem.

2. You are working with a directory table named associated with an external stage containing a large number of small JSON files. You need to process only the files containing specific sensor readings based on a substring match within their filenames (e.g., files containing 'temperature' in the filename). You also want to load these files into a Snowflake table 'sensor_readings. Consider performance and cost-effectiveness. Which of the following approaches is the MOST efficient and cost-effective to achieve this? Choose TWO options.

A) Create a masking policy based on filenames to control which files users can see.
B) Load all files from the stage using 'COPY INTO' into a staging table, and then use a Snowflake task to filter and move the relevant records into the 'sensor_readingS table.
C) Use 'COPY INTO' with the 'PATTERN' parameter, constructing a regular expression that includes the substring match against the filename obtained from the directory table's 'relative_path' column.
D) Use a Python UDF to iterate through the files listed in , filter based on filename, and then load each matching file individually using the Snowflake Python Connector.
E) Create a view on top of the directory table that filters the 'relative_patW based on the substring match, and then use 'COPY INTO' with the 'FILES' parameter to load the filtered files.

3. You are tasked with creating a system to monitor the data quality of a 'SALES DATA" table. The table is updated daily with new sales records, and you need to ensure that the 'SALE AMOUNT column always contains positive values. You decide to use Snowflake tasks and streams for this purpose. Consider the following Snowflake script. What is the most appropriate way to modify the 'SALES DATA' table so the task has a 'WHEN' clause that runs only if the 'SALE AMOUNT has negative values? Assume the stream 'SALES DATA STREAM' is properly configured on 'SALES DATA'.

A)

B)

C)

D)

E)

4. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

5. A data engineer is tasked with creating a Snowpark Python UDF to perform sentiment analysis on customer reviews. The UDF, named 'analyze_sentiment' , takes a string as input and returns a string indicating the sentiment ('Positive', 'Negative', or 'Neutral'). The engineer wants to leverage a pre-trained machine learning model stored in a Snowflake stage called 'models'. Which of the following code snippets correctly registers and uses this UDF?

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

Solutions: