Snowflake DEA-C02 Exam : 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. You have a Snowflake task that executes a complex stored procedure. This stored procedure performs several UPDATE statements on a large table. After enabling the 'QUERY TAG' parameter, you notice that the task history in Snowflake shows frequent suspensions due to exceeding warehouse resource limits. The warehouse is already scaled to the largest size. Which combination of the following strategies would BEST address this issue and minimize task suspensions, assuming you CANNOT further scale the warehouse?

A) Set a lower 'SUSPEND TASK AFTER N FAILURES' value to proactively suspend the task before it consumes excessive resources.
B) Break down the stored procedure into smaller, more manageable transactions and commit changes more frequently. Consider utilizing batch processing techniques.
C) Optimize the UPDATE statements in the stored procedure to reduce resource consumption by using techniques such as clustering keys, partitioning and avoiding full table scans.
D) Implement a retry mechanism within the task's SQL code to automatically retry failed UPDATE statements after a short delay.
E) Increase the 'ERROR ON N parameter for the task to allow for more consecutive timeouts before the task is suspended.

2. A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?

A) Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.
B) Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.
C) Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
D) Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.
E) Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.

3. You need to implement a data masking solution in Snowflake for a table 'CUSTOMER DATA' containing PII. The requirement is to mask the email address based on the user's role: if the user is in 'ANALYST ROLE , the email address should be partially masked (e.g., 'a @example.com'), otherwise, it should be fully masked (e.g., @ .com'). Which of the following masking policy definitions and subsequent actions will correctly implement this?

A) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, fully mask using 'REGEXP REPLACE. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
B) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, return original value. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
C) Create two separate masking policies, one for 'ANALYST_ROLE' and one for all other roles. Apply both policies to the 'EMAIL' column of 'CUSTOMER DATA'. Grant the 'APPLY MASKING POLICY privilege on the 'CUSTOMER DATA' table to the 'ANALYST_ROLE.
D) Create a masking policy 'email_mask' using 'REGEXP_REPLACE to replace the first part of the email with asterisks if the current role is not 'ANALYST_ROLE' , otherwise use 'LEFT and ' REGEXP_REPLACE to mask only part of the username. Apply this policy to the 'EMAIL ' column of 'CUSTOMER DATA'.
E) Create a masking policy 'email_mask' that always fully masks the email address. Grant the 'UNMASK' privilege on the 'EMAIL' column to the 'ANALYST ROLE

4. You have a large dataset of JSON documents stored in AWS S3, each document representing a customer order. You want to ingest these documents into Snowflake using Snowpipe and transform the nested 'address' field into separate columns in your target table. Considering data volume, complexity, and cost efficiency, which approach is MOST suitable?

A) Pre-process the JSON documents using an external compute service (e.g., AWS Lambda) to flatten the 'address' field before ingesting into Snowflake via Snowpipe.
B) Use a COPY INTO statement with a transform clause to flatten the 'address' field during ingestion.
C) Create an external table on the S3 bucket and then use CREATE TABLE AS SELECT (CTAS) to transform the data.
D) Use Snowpipe with a user-defined function (UDF) written in Python to parse the JSON and flatten the 'address' field.
E) Use Snowpipe to ingest the raw JSON data into a VARIANT column, then create a view that flattens the 'address' field.

5. You are responsible for monitoring a critical data pipeline that loads data from an external Kafka topic into a Snowflake table 'ORDERS' Data anomalies have been frequently observed, impacting downstream reporting. You want to implement a solution that proactivelyidentifies and alerts on data quality issues such as missing values, invalid formats, and unexpected data distributions. Which combination of Snowflake features and approaches would be MOST effective for achieving this objective with minimal performance overhead on the pipeline itself?

A) Implementing custom SQL-based data quality checks within a scheduled Snowflake task that runs after the data load and writing results to an audit table for monitoring.
B) Using Snowflake's 'VALIDATE' table function after the data load to check for data corruption and then trigger alerts based on the validation results.
C) Creating a separate Snowflake pipeline that reads from the same Kafka topic, performs data quality checks in real-time using Snowpipe and streams the results to an alert system.
D) Leveraging Snowflake's Data Governance features along with Snowpark UDFs to define and enforce data quality rules at the time of ingestion using a Python- based library like Great Expectations, configured to trigger alerts through Snowflake Notifications.
E) Employing Snowflake's built-in statistics and histogram features to analyze data distribution in the 'ORDERS' table and configure alerts based on deviations from historical patterns, combined with a Snowflake Native App for data quality reporting.

Solutions: