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

1. A data engineer is using Snowpark Python to build a data pipeline. They need to define a UDF that uses a pre-trained machine learning model stored as a file in a Snowflake stage. The UDF should receive batches of data for scoring. Which of the following is the MOST efficient way to implement this, minimizing data transfer and execution time?

A) Load the model from the stage into a DataFrame, then use 'df.mapPartitionS to apply the model to each partition.
B) Create a UDF with gudf(packages=['snowflake-snowpark-python', 'scikit-learn'], input_types=[ArrayType(StringType())], return_type=FloatType(), replace=True, is_permanent=True, and load the model within the UDF's initialization using 'session.file.get' .
C) Use 'session.read.parquet' to load the model file directly into a Snowpark DataFrame and then use 'DataFrame.foreach' to process each row.
D) Create a UDF that reads the model from the stage for each row that is passed to it using 'session.file.get' inside the UDF's execution logic.
E) Use '@vectorized' decorator from Snowpark to process each batch of data passed to the UDF and load the model inside it. Specify the appropriate data types in the decorator.

2. A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

A) Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
B) Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.
C) Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.
D) Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
E) Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.

3. You have implemented a row access policy on a 'products' table to restrict access based on the user's group. The policy uses a mapping table 'user_groups' to determine which products a user is allowed to see. After implementing the policy, users are reporting significant performance degradation when querying the 'products' table. What are the MOST likely causes of this performance issue, and what steps can you take to mitigate them? Select all that apply.

A) The row access policy is interfering with Snowflake's data pruning capabilities. Ensure that the policy expression can be evaluated efficiently by Snowflake's query optimizer by using the 'USING' clause of the ROW ACCESS POLICY.
B) The users do not have sufficient privileges to access the 'user_groups' table. Grant the necessary SELECT privileges to the users on the 'user_groupS table.
C) The row access policy is causing full table scans on the 'products' table. Review the query patterns and consider adding clustering keys to the 'products' table to improve data access patterns.
D) The 'user_groups' table is not properly indexed, causing slow lookups during policy evaluation. Create an index on the 'username' and 'group' columns of the 'user_groups' table.
E) The row access policy is overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.

4. You are building a data pipeline to ingest clickstream data into Snowflake. The raw data is landed in a stage and you are using a Stream on this stage to track new files. The data is then transformed and loaded into a target table 'CLICKSTREAM DATA. However, you notice that sometimes the same files are being processed multiple times, leading to duplicate records in 'CLICKSTREAM DATA. You are using the 'SYSTEM$STREAM HAS DATA' function to check if the stream has data before processing. What are the possible reasons this might be happening, and how can you prevent it? (Select all that apply)

A) The 'SYSTEM$STREAM HAS DATA' function is unreliable and should not be used for production data pipelines. Use 'COUNT( on the stream instead.
B) The transformation process is not idempotent. Even with the same input files, it produces different outputs each time it runs.
C) The COPY INTO command used to load the files into Snowflake has the 'ON ERROR = CONTINUE option set, allowing it to skip corrupted files, causing subsequent processing to pick them up again.
D) The auto-ingest notification integration is configured incorrectly, causing duplicate notifications to be sent for the same files. This is particularly applicable when using cloud storage event triggers.
E) The stream offset is not being advanced correctly after processing the files. Ensure that the files are consumed completely and a DML operation is performed to acknowledge consumption.

5. You are tasked with optimizing the performance of a Snowflake virtual warehouse used for running several types of queries: short- running analytical queries with strict latency requirements, long-running batch data transformations, and ad-hoc queries from data scientists. The workload is unpredictable, and the team wants to minimize queueing and maximize resource utilization. Which warehouse configuration would be MOST appropriate to handle this mixed workload, minimizing cost and maximizing performance?

A) A multi-cluster warehouse with a scaling policy of 'Standard' and a minimum of 1 and maximum of 3 clusters with auto-suspend set to 10 minutes.
B) Three separate warehouses: a Medium warehouse for analytical queries, a Large warehouse for batch transformations, and an X-Small warehouse for ad-hoc queries.
C) A single X-Large warehouse with auto-suspend set to 5 minutes.
D) A multi-cluster warehouse with a scaling policy of 'Economy' and a minimum of 1 and maximum of 2 clusters with auto-suspend set to 5 minutes.
E) A single Small warehouse with auto-suspend set to 60 minutes.

Solutions: