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

1. You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

A) Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.
B) Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.
C) Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.
D) Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.
E) Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.

2. You have a Snowflake table 'CUSTOMER DATA' with the following schema:

Your downstream application requires a new column 'PHONE NUMBER VARCHAR(20)' in the 'CUSTOMER DATA' table. You need to add this column without causing any downtime or disruption to the application. What is the MOST appropriate approach?

A) create a temporary table which is a copy of CUSTOMER_DATA but with the additional column. swap the CUSTOMER_DATA and CUSTOMER DATA TEMP tables.
B) Create a view 'CUSTOMER on top of 'CUSTOMER DATA' that includes the new 'PHONE NUMBER column with a default value. Update the application to use the view instead of the table.
C) Create a new stream on the table 'CUSTOMER DATA'. Then, create a new table 'CUSTOMER DATA NEW' with the extra PHONE NUMBER column as part of the DDL. Use the stream and table to update data from the old to new table, Then, drop "CUSTOMER_DATA' , and rename to 'CUSTOMER DATA'.
D) Execute 'ALTER TABLE CUSTOMER DATAADD COLUMN PHONE NUMBER VARCHAR(20);'. This will add the column with a NULL value for existing rows.
E) Create a new table 'CUSTOMER DATA NEW' with the new column, copy data from 'CUSTOMER DATA' to 'CUSTOMER DATA NEW, drop "CUSTOMER DATA', and rename 'CUSTOMER DATA NEW' to 'CUSTOMER DATA'.

3. A data engineer is tasked with migrating data from a large on-premise Hadoop cluster to Snowflake using Spark. The Hadoop cluster contains nested JSON dat a. To optimize performance and minimize data transformation in Spark, what is the most efficient approach to read the JSON data into a Spark DataFrame and write it directly to a Snowflake table?

A) Use the 'STORAGE_INTEGRATION' feature in Snowflake to directly access the JSON files in Hadoop (via an external stage) and load the data without using Spark at all.
B) Use the Snowflake Spark connector with the 'inferSchema' option set to 'true' when reading the JSON data. This allows Spark to automatically infer the schema and write directly to Snowflake.
C) Read the JSON data as strings and utilize Snowflake's 'PARSE JSON' function within a Spark SQL query to transform and load the data into a variant column in Snowflake.
D) Define a schema manually in Spark, then read the JSON data into a Spark DataFrame. Use the Snowflake Spark connector to write the data to Snowflake, specifying the schema explicitly.
E) Read the JSON data as text files, then use Spark to parse and flatten the JSON structure before writing to Snowflake using the Snowflake JDBC connector.

4. Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

A) Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.
B) Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION
C) Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.
D) Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
E) Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.

5. You are implementing a data pipeline in Snowpark that reads data from an external stage (e.g., AWS S3) and performs complex transformations, including joins with large Snowflake tables. You notice that the pipeline's performance is significantly slower than expected, despite having sufficient warehouse resources. Which of the following actions would MOST likely improve the performance of the Snowpark data pipeline?

A) Ensure that the external stage is properly configured with appropriate data formats (e.g., Parquet) and partitioning schemes that align with the join keys.
B) Optimize the SQL joins within the Snowpark DataFrame operations by using broadcast joins when appropriate and ensuring correct join key data types.
C) Increase the warehouse size to the largest available option (e.g., X-Large or larger).
D) Persist the DataFrame representing the data from the external stage using 'df.cache()' before performing the joins.
E) Reduce the number of partitions in the DataFrame representing the data from the external stage using 'df.repartition(l )'.

Solutions: