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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

A) All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
B) Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
C) Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.
D) External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.
E) Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.

2. Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?

A) The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.
B) The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.
C) The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
D) The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
E) The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.

3. A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A) Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
B) Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
C) Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
D) Create separate tables for each "PRODUCT_ID' to improve query performance.
E) Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.

4. Consider a scenario where you need to transform data in a Snowflake table using a complex custom transformation logic best implemented in Java'. You decide to use a Snowpark Java UDF. You've packaged your Java code into a JAR file and uploaded it to an internal stage named Which of the following steps are necessary and correctly ordered to deploy and use this Java UDF within Snowflake?

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

5. You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

A) Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.
B) Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.
C) Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.
D) Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.
E) Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.

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