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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table named 'ORDERS clustered on 'ORDER DATE. After a significant data load, you want to evaluate the effectiveness of the clustering. Which of the following SQL queries, using Snowflake system functions, will provide insights into the clustering depth and overlap of micro-partitions in the 'ORDERS' table, specifically helping you identify whether re-clustering is necessary? Assume that the table

A)

B)

C)

D)

E)

2. You are tasked with loading a large dataset (50TB) of JSON files into Snowflake. The JSON files are complex, deeply nested, and irregularly structured. You want to maximize loading performance while minimizing storage costs and ensuring data integrity. You have a dedicated Snowflake virtual warehouse (X-Large).
Which combination of approaches would be MOST effective?

A) Load the JSON data using the COPY INTO command with gzip compression. Create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use materialized views to improve query performance.
B) Use Snowpipe with auto-ingest, create a single VARIANT column in your target table, and rely solely on Snowflake's automatic schema detection.
C) Pre-process the JSON data using a Python script with Pandas to flatten the structure and convert it into a relational format like CSV. Then, load the CSV files using the COPY INTO command with gzip compression.
D) Load the JSON data using the COPY INTO command with no pre-processing. Create a VIEW on top of the raw VARIANT column to flatten the data for querying.
E) Use Snowpipe with auto-ingest, create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use search optimization on those projected columns.

3. A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' , 'CUSTOMERS' , and 'PRODUCTS. The 'ORDERS' table contains millions of records and is frequently joined with 'CUSTOMERS' (containing customer demographics) and 'PRODUCTS' (containing product details). The initial query uses standard JOIN syntax, but performance is slow. The query retrieves order details along with customer and product information, filtering by a specific date range in the 'ORDERS' table and a customer segment in the 'CUSTOMERS table. Which optimization strategy would be MOST effective for significantly improving query performance?

A) Apply clustering keys to the 'ORDERS table based on the date column used in the WHERE clause and clustering keys to the 'CUSTOMERS' table on the customer segment column. Also create appropriate indexes.
B) Replace the standard JOINs with LATERAL FLATTEN operations.
C) Convert the entire dataset into a single VARIANT column and query using JSON path expressions.
D) Increase the virtual warehouse size to X-LARGE without analyzing the query profile.
E) Create materialized views that pre-join the 'ORDERS', 'CUSTOMERS, and 'PRODUCTS tables and filter based on common criteria.

4. A Snowflake table 'ORDERS' is clustered on the 'ORDER DATE column. After several months, you notice that many micro-partitions contain data from a wide range of 'ORDER DATE values, and query performance on date range filters is degrading. Which of the following actions could improve performance and reduce the overlap in micro-partitions?

A) Change the ORDER DATE column to VARCHAR and then cluster.
B) Change the clustering key to a composite key including ORDER DATE and another relevant column.
C) Drop and recreate the table with a different clustering key.
D) Increase the virtual warehouse size used for loading data into the "ORDERS' table.
E) Run 'OPTIMIZE TABLE ORDERS' to recluster the table.

5. You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A) Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
B) Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.
C) Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.
D) Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
E) Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.

Solutions: