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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineering team is building a real-time fraud detection system. They have a large 'TRANSACTIONS table that grows rapidly. They need to calculate the average transaction amount per merchant daily. The following query is used:

This query is run every hour and is performance-critical. Which of the following materialized view definitions would provide the BEST performance improvement, considering the need for near real-time data and minimal latency?

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

2. You are tasked with building a data pipeline that incrementally loads data from an external cloud storage location (AWS S3) into a Snowflake table named 'SALES DATA'. You want to optimize the pipeline for cost and performance. Which combination of Snowflake features and configurations would be MOST efficient and cost-effective for this scenario, assuming the data volume is substantial and constantly growing?

A) Use a Snowflake Task to regularly truncate and reload 'SALES DATA" from S3 using COPY INTO. This ensures data consistency.
B) Use a Snowflake Task scheduled every 5 minutes to execute a COPY INTO command from S3, with no file format specified, assuming the data is CSV and auto-detection will work.
C) Develop a custom Python script that uses the Snowflake Connector for Python to connect to Snowflake and execute a COPY INTO command. Schedule the script to run on an EC2 instance using cron.
D) Create an external stage pointing to the S3 bucket. Create a Snowpipe with auto-ingest enabled, using an AWS SNS topic and SQS queue for event notifications. Configure the pipe with an error notification integration to monitor ingestion failures.
E) Employ a third-party ETL tool to extract data from S3, transform it, and load it into Snowflake using JDBC. Schedule the ETL process using the tool's built-in scheduler.

3. You have implemented a masking policy on the 'SSN' column of the 'EMPLOYEES' table. You now need to suspend the masking policy temporarily for a specific batch job that requires access to the unmasked data'. What is the recommended way to achieve this without dropping the masking policy or altering the user's role?

A) Set the 'DISABLE MASKING' session parameter to 'TRUE' for the batch job session. This will temporarily disable all masking policies.
B)

C) Grant the 'APPLY MASKING POLICY privilege to the user running the batch job, allowing them to bypass the masking policy.
D) Create a new role with 'ACCOUNTADMIN' privileges, assign this role to the batch job process during the execution, and then revert back to the original role after the job is done.
E) Create a temporary view on the 'EMPLOYEES' table without the 'SSN' column, grant access to the view for the batch job, and drop the view after the job is complete.

4. You're designing a Snowpark Scala stored procedure that must execute a series of complex data quality checks on a Snowflake table.
These checks involve multiple steps, including validating data types, checking for null values, and verifying data consistency against external reference data'. You want to ensure that the stored procedure is resilient to errors, provides detailed logging, and can be easily monitored. Which of the following approaches would be the MOST robust and scalable for handling errors and logging within this Snowpark Scala stored procedure?

A) Use Scala's 'Option' type to handle potential null values and exceptions. Return a string message indicating success or failure for each check. Log these messages using 'System.out.println'.
B) Use Scala's 'Try' monad to handle exceptions, mapping successes to informational messages and failures to error messages. Log these messages using Snowflake's event tables.
C) Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.
D) Rely on Snowflake's built-in error handling and logging mechanisms. If an error occurs, the stored procedure will automatically fail, and the error details can be retrieved from Snowflake's query history.
E) Implement a custom logging framework within the Scala stored procedure that writes detailed logs to a dedicated Snowflake table. Use try-catch blocks to handle exceptions and log error details, including timestamps, error codes, and relevant data values. Use Snowflake's 'SYSTEM$LAST QUERY ID()' function to track query lineage.

5. You are developing a Snowpark Python application that processes data from a large table. You want to optimize the performance by leveraging Snowpark's data skipping capabilities. The table 'CUSTOMER ORDERS is partitioned by 'ORDER DATE. Which of the following Snowpark operations will MOST effectively utilize data skipping during data transformation?

A) Creating a new DataFrame with only the columns needed using 'ORDER_DATE', 'ORDER_AMOUNT')' before any filtering operations.
B) Applying a filter >= '2023-01-01') & (col('ORDER_DATE') <= '2023-03-31'))' after performing a complex join operation.
C) Executing 'df.collect()' to load the entire table into the client's memory before filtering.
D) Applying a filter '2023-01-01') & '2023-03-31'))' before performing any join or aggregation operations.
E) Using the 'cache()' method on the DataFrame before filtering by 'ORDER DATE

Solutions: