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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are a data engineer responsible for data governance in a Snowflake environment. Your company has implemented data classification using tags to identify sensitive data'. The compliance team has requested a report detailing all tables and columns that contain PII data, specifically including the tag name, tag value, the fully qualified name of the table, and the column name. You have the necessary privileges to access the Snowflake metadata views. Which of the following queries would provide the MOST comprehensive and accurate report, considering performance and ease of understanding?

A)

B)

C)

D)

E)

2. A data engineering team is building a real-time data pipeline in Snowflake. Data arrives continuously and needs to be processed with minimal latency. The team is using Snowflake Streams and Tasks for incremental data processing. However, they are encountering issues where the tasks are sometimes skipped or delayed, leading to data inconsistencies. Which combination of actions would BEST address these issues and ensure reliable near real-time data processing?

A) Disable task scheduling and rely solely on Snowflake's Auto-Resume feature for warehouses. This simplifies the pipeline and reduces the chance of errors.
B) Adjust the ' ERROR_INTEGRATION' parameter on the task definition to send notifications when tasks fail. This allows for manual intervention but does not prevent skipping.
C) Monitor the 'TASK HISTORY view regularly to identify skipped or delayed tasks and manually re-run them as needed. This is a reactive approach and does not prevent future occurrences.
D) Increase the warehouse size to ensure sufficient compute resources. This will prevent tasks from being skipped due to resource contention.
E) Configure the tasks to run using a serverless compute model (Snowflake-managed compute). Ensure the parameter is set to a higher value and implement error handling within the task using TRY/CATCH blocks.

3. A data engineer is using the Snowflake Spark connector to read a large table from Snowflake into a Spark DataFrame. The table contains a 'TIMESTAMP NTT column. After loading the data, the engineer observes that the values in the 'TIMESTAMP NTZ' column are not preserved accurately when retrieved from the DataFrame. What are the potential issues and what configurations can be adjusted in Snowflake to improve the result?

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

4. You are tasked with building a data pipeline that ingests customer interaction data from multiple microservices using Snowpipe Streaming. Each microservice writes data in JSON format to its own Kafka topic. You need to design an efficient and scalable solution to ingest this data into a single Snowflake table, while ensuring data integrity and minimizing latency. Consider these constraints: 1. High data volume with variable ingestion rates. 2. The need to correlate data from different microservices based on a common 'customer id'. 3. Potential for schema evolution in the microservices. Given these requirements and constraints, which of the following architectural approaches, leveraging Snowpipe Streaming features and Snowflake capabilities, would be the MOST appropriate and robust?

A) Develop a single Snowpipe Streaming client that consumes data from all Kafka topics, using a transformation function to route the data to the correct table based on the topic name. Use Snowflake's clustering key on 'customer _ id' for efficient querying.
B) Develop a Spark Streaming application that reads data from Kafka, transforms it, and then uses the Snowflake Connector for Spark to write the data to Snowflake in micro-batches.
C) Use a single Snowpipe Streaming client to ingest data from all Kafka topics into a single VARIANT column in the Snowflake table. Then, use Snowflake's external functions to transform and load the data into the final target table based on the 'customer_id'
D) Create a separate Snowpipe Streaming client for each Kafka topic, ingesting data into separate staging tables. Then, use a scheduled task to merge the data into the final target table based on 'customer id'.
E) Implement a custom Kafka Connect connector that directly writes data to Snowflake using Snowpipe Streaming. The connector should handle schema evolution and routing based on topic name. Define a clustering key on the Snowflake table on the 'customer id'

5. You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

A) The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.
B) Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.
C) The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.
D) The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.
E) The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.

Solutions: