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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a requirement to create a UDF in Snowflake that transforms data based on a complex set of rules defined in an external Python library. The library requires specific dependencies. You also need to ensure the UDF is secure and that the code is not visible to unauthorized users. Which of the following steps MUST be taken to achieve this?

A) Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.
B) Create an external function pointing to an AWS Lambda function or Azure Function that hosts the Python code and its dependencies. Secure the external function using API integration and role-based access control.
C) Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.
D) Create a Snowflake Anaconda environment specifying the required Python library dependencies. Then, create a Python UDF, reference the Anaconda environment, and use the 'SECURE' keyword.
E) Upload the Python library and its dependencies as internal stages. Create a Java UDF that executes the Python code using the 'ProcessBuilder' class. Mark the Java UDF as 'SECURE'

2. You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

A) Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.
B) Prompt the user for the PostgreSQL username and password each time the script is executed.
C) Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
D) Hardcode the PostgreSQL username and password directly into the Python script.
E) Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.

3. You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

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

4. You have implemented external tokenization for a sensitive data column in Snowflake using a UDF that calls an external API. After some time, you discover that the external tokenization service is experiencing intermittent outages, causing queries using the tokenized column to fail. What is the BEST approach to mitigate this issue and maintain data availability while minimizing the risk of exposing the raw data?

A) Modify the tokenization UDF to cache tokenization mappings locally within the Snowflake environment. When the external service is unavailable, the UDF can use the cached values.
B) Implement a masking policy on the column that returns the raw data when the tokenization UDF is unavailable, detected by catching exceptions within the policy logic.
C) Implement a try-catch block within the UDF. In the catch block, return a pre-defined static token value (same value always) instead of attempting to call the external tokenization service. You can't return the raw value.
D) Replicate the tokenized table to another Snowflake region and switch to the replica during outages of the primary region. The tokenization service is guaranteed to be available in at least one region.
E) Implement a try-catch block within the UDF. In the catch block, return a pre-defined, non-sensitive default value instead of attempting to call the external tokenization service. You can't return the raw value.

5. You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?

A) Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.
B) Disable auto-tuning for the warehouse to ensure consistent performance
C) Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
D) Increase the size of the Snowflake warehouse used for the Snowpark session.
E) Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.

Solutions: