Sample Questions from SnowPro Advanced Architect / Data Scientist / Data Engineer ACO-x (various) by SnowFlake

Preview a few questions below — answers are revealed when you take the exam.

1. A team needs to optimize a Snowflake data warehouse for high-concurrency read operations while maintaining data integrity. What approach should they take?

   • Implementing a multi-cluster warehouse setup with auto-scaling enabled to handle peak loads and using result caching to reduce query execution time for repeated read operations.
   • Using a single large warehouse with manual scaling based on load predictions and employing materialized views to precompute and store results of frequent queries.
   • Adopting a serverless compute model for all read operations and utilizing transactional tables to ensure data consistency across concurrent sessions.
   • Configuring a dedicated warehouse for read operations with a fixed size and using time travel features to manage data versioning and integrity.

2. Consider the situation where a data scientist needs to perform complex machine learning model training on large datasets stored in Snowflake. Which method solves it best?

   • Utilizing Snowflake's native support for Python and integrating with external machine learning libraries to perform model training directly within the Snowflake environment.
   • Exporting data from Snowflake to an external data lake, performing model training using a distributed computing framework, and then importing the trained model back into Snowflake.
   • Using Snowflake's secure data sharing feature to share datasets with an external machine learning platform for training and then importing the results back into Snowflake.
   • Implementing a custom ETL process to preprocess data in Snowflake and then using an external machine learning service for model training, with results stored in Snowflake.

3. What strategy should be applied when designing a data pipeline in Snowflake to ensure minimal data latency and high availability?

   • Implementing a real-time data ingestion pipeline using Snowflake's streaming capabilities combined with a multi-cluster warehouse setup for immediate data processing and availability.
   • Using batch processing for data ingestion with scheduled tasks to load data into Snowflake, ensuring data is available for querying within a predefined time frame.
   • Adopting a hybrid approach where critical data is ingested in real-time, while non-critical data is processed in batches to balance resource utilization and data availability.
   • Configuring Snowflake to use external stage locations for data ingestion, with automated scripts to periodically load data into Snowflake for querying.

4. How would you decide the best method for securing sensitive data within a Snowflake environment?

   • Employing dynamic data masking and row-level security policies to restrict access to sensitive data based on user roles and permissions, combined with encryption at rest and in transit.
   • Using Snowflake's native encryption features to encrypt all data at rest and in transit, without implementing additional access control measures.
   • Applying static data masking to all sensitive data fields before loading them into Snowflake, ensuring that no sensitive information is stored in the database.
   • Relying solely on network security measures, such as VPNs and firewalls, to protect data in transit, without implementing encryption or access control policies within Snowflake.

5. What configuration is most appropriate for a Snowflake environment that requires high availability and disaster recovery capabilities?

   • Configuring Snowflake in a multi-region setup with automatic failover capabilities, ensuring that data is replicated across regions for disaster recovery.
   • Using a single-region setup with manual backup and restore procedures for disaster recovery, relying on Snowflake's built-in replication features for high availability.
   • Implementing a hybrid cloud strategy where critical data is stored in Snowflake and non-critical data is stored in an external cloud provider, with manual failover procedures in place.
   • Relying solely on Snowflake's native replication features for high availability, without configuring additional disaster recovery measures.