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

1. 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) Hardcode the PostgreSQL username and password directly into the Python script.
B) 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.
C) Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
D) Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.
E) Prompt the user for the PostgreSQL username and password each time the script is executed.

2. You are implementing a data pipeline in Snowpark that reads data from an external stage (e.g., AWS S3) and performs complex transformations, including joins with large Snowflake tables. You notice that the pipeline's performance is significantly slower than expected, despite having sufficient warehouse resources. Which of the following actions would MOST likely improve the performance of the Snowpark data pipeline?

A) Reduce the number of partitions in the DataFrame representing the data from the external stage using 'df.repartition(l )'.
B) Ensure that the external stage is properly configured with appropriate data formats (e.g., Parquet) and partitioning schemes that align with the join keys.
C) Increase the warehouse size to the largest available option (e.g., X-Large or larger).
D) Optimize the SQL joins within the Snowpark DataFrame operations by using broadcast joins when appropriate and ensuring correct join key data types.
E) Persist the DataFrame representing the data from the external stage using 'df.cache()' before performing the joins.

3. 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 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.
C) 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.
D) Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.
E) 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.

4. You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

A) Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE
B) Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
C) Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
D) Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
E) Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.

5. Consider the following scenario: You are managing a Snowflake environment where users are running various queries with varying resource demands. You observe frequent warehouse resizing operations, leading to performance fluctuations and increased costs. Which of the following strategies, when implemented together, would BEST stabilize warehouse performance and minimize unnecessary resizing?

A) Disable auto-suspend for the warehouse to prevent it from shutting down and causing performance delays. Force users to manually resize the warehouse as needed using SALTER WAREHOUSE commands.
B) Monitor query history using Snowflake's web interface and identify query patterns that consistently require larger resources; recommend users refactor those queries. Set the warehouse auto-suspend to a very short duration (e.g., 1 minute) to ensure resources are released quickly when idle.
C) Implement Query Tagging to categorize queries based on resource consumption. Analyze resource utilization patterns for different query categories. Adjust warehouse size and multi-cluster configuration based on these patterns, ensuring that a reasonable number of concurrent queries for each workload type is met.
D) Implement Resource Monitors to limit the daily credit consumption of the warehouse. Increase the warehouse size to accommodate all possible query demands and set the auto-suspend to a longer duration (e.g., 60 minutes).
E) Enable Query Acceleration Service (QAS) for the warehouse. Set the warehouse size to Medium, regardless of the actual workload demands, and rely solely on QAS to handle performance variations.

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