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Snowflake DEA-C02 Exam Overview:

Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?

A) Increase the virtual warehouse size to 'LARGE or 'XLARGE.
B) Redesign the dashboard to minimize the data being displayed at once to the user.
C) Cluster the 'CUSTOMER ORDERS table on 'ORDER DATE' and 'REGION'.
D) Create an index on the 'ORDER DATE column.
E) Create a materialized view that pre-aggregates the data by 'ORDER DATE and 'REGION'

2. 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) 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.
B) 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).
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) 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.
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.

3. A data provider wants to share a large dataset (several TB) with multiple consumers. The dataset is updated daily. The provider wants to minimize the cost associated with data sharing and ensure that consumers receive consistent data'. Which of the following strategies would be the MOST cost-effective and maintainable?

A) Create a data share with views that point to the base tables, and clone the base tables daily into a separate 'staging' database before sharing.
B) Create a data share with views that point to the base tables. Use time travel to allow consumers to query data from a specific point in time before the daily update.
C) Create a data share and grant access to all consumers directly on the base tables.
D) Create a data share containing external tables pointing to data stored in cloud storage (e.g., AWS S3), updated daily using a pipeline.
E) Create a data share and create a separate database for each consumer, cloning the data into each consumer's database daily.

4. You're building a data pipeline that ingests JSON data from URLs representing real-time weather information. The data structure varies slightly between different weather providers, but all contain a 'location' object with 'city' and 'country' fields, and a 'temperature' field. You need to create a generic function that can handle these variations and extract the location and temperature, returning a flattened JSON object with keys 'city', 'country', and 'temperature'. You want to avoid explicit schema definition and take advantage of Snowflake's VARIANT data type flexibility Given the following sample JSON structures, which approach will best accomplish this?

A) Define a Snowflake stored procedure that uses 'SYSTEM$URL_GET to fetch the JSON data, then uses conditional logic with 'TRY TO BOOLEANS and STRY TO DATE to handle different data types. The stored procedure constructs a new JSON object with 'city', 'country', and 'temperature' fields using 'OBJECT_CONSTRUCT.
B) Define a Snowflake external function (UDF) that fetches the JSON data using a Python library like 'requests' or The function then parses the JSON and extracts the required fields, handling potential missing fields using 'try...except' blocks. The function returns a JSON string representing the flattened object.
C) Create a pipe that uses 'COPY INTO to ingest JSON data directly from the URLs into a VARIANT column. The 'FILE FORMAT object is configured to use = TRUE to handle different data types. Post ingestion create a view to query data.
D) Define a Snowflake view that selects from a table containing the URLs, using 'SYSTEM$URL GET to fetch the JSON data and to extract the 'city', 'country', and 'temperature' fields. Use 'TRY_CAST to convert the 'temperature' to a numeric type.
E) Create a Snowflake external function written in Java that uses 'java.net.lJRL' to fetch the JSON data and 'com.fasterxml.jackson.databind' library to parse it. Use Jackson's 'JsonNode' to navigate the varying JSON structure and extract 'city', 'country', and 'temperature' fields. Return a JSON string of the result.

5. A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:

Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.

A) The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.
B) The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.
C) The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.
D) The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.
E) The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.

Solutions: