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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A Snowflake data pipeline ingests data from multiple external sources into a RAW DATA table. A transformation process then moves the data to a ANALYTICS DATA table, applying several complex UDFs written in Java and Python for data cleansing and enrichment. Performance is significantly slower than expected. Which combination of techniques would BEST improve the performance of this transformation pipeline?

A) Reduce the number of UDF calls by consolidating them into a single, more complex UDF. Replace the transformation pipeline with a series of COPY INTO statements.
B) Implement data partitioning in the RAW DATA table based on ingestion time and switch to using stored procedures instead of transformation pipelines.
C) Increase the virtual warehouse size and re-cluster the ANALYTICS DATA table based on the most frequently filtered columns.
D) Use external functions instead of UDFs to offload the processing to an external compute environment and configure auto-scaling for the virtual warehouse.
E) Rewrite the UDFs in SQL or Snowpark Python/Java for better integration with the Snowflake engine and leverage vectorization where possible; cache intermediate results using temporary tables.

2. You are designing a data warehouse for an e-commerce company. One of the requirements is to provide fast analytics on order fulfillment times by region. You have two tables: 'ORDERS: Contains order information, including ID, 'ORDER DATE, 'REGION ID, and 'FULFILLMENT DATE. 'REGIONS': Contains region information, including 'REGION ID' and Due to the large size of the 'ORDERS' table and the complexity of calculating fulfillment times, you decide to use materialized views.
Which of the following combinations of materialized view definition and Snowflake features would BEST optimize query performance and minimize data staleness for this scenario? Choose two options.

A) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates the difference between 'FULFILLMENT DATE' and 'ORDER DATE as , and groups by REGION_NAME. Cluster the view by ' REGION_NAME.
B) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates 'FULFILLMENT TIME', and groups by 'REGION NAME'. Do not specify a clustering key.
C) Partition the 'ORDERS' table by 'ORDER_DATE and create a materialized view that calculates 'FULFILLMENT_TIME grouped by REGION_NAME , clustering by 'ORDER DATE'
D) Use Snowflake's search optimization service on the 'ORDERS' table instead of creating a materialized view.
E) create a materialized view that joins 'ORDERS' and 'REGIONS', calculates 'FULFILLMENT_TIME' grouped by 'REGION_NAME, and cluster by 'REGION NAM Configure incremental data refreshes.

3. You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

A) Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.
B) Prompt the user for the PostgreSQL username and password each time the script is executed.
C) Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
D) Hardcode the PostgreSQL username and password directly into the Python script.
E) Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.

4. You are implementing row access policies on a 'SALES DATA table to restrict access based on the 'REGION' column. Different users are allowed to see data only for specific regions. You have a mapping table 'USER REGION MAP' with columns 'USERNAME' and 'REGION'. You want to create a row access policy that dynamically filters the 'SALES DATA' based on the user and their allowed region. Which of the following options represents a correct approach to create and apply this row access policy?

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

5. You have a table 'ORDERS in your Snowflake database. You are implementing a new data transformation pipeline. Before deploying the pipeline to production, you want to validate the changes in a development environment. You decide to use Time Travel to create a snapshot of the 'ORDERS' table before the transformation and compare it with the transformed data'. Which sequence of SQL commands would best facilitate this validation, assuming your development database and schema structure mirrors production?

A)

B)

C)

D)

E)

Solutions: