Free Practice Questions for Snowflake DEA-C02 Certification

● Outline considerations for data loading ● Define data loading features and potential impacts

● Required file formats ● Schema detection using INFER_SCHEMA for table design and data analysis ● Ingestion of structured, semi-structured, and unstructured data ● Implementation of stages and file formats ○ Manage storage integrations configurations ○ Manage encryption (pre-scoped URLs, server-side, or client-side) ○ Manage compression and parsing strategies ● Extract metadata from staged files

● Identify causes of ingestion errors ● Determine resolutions for ingestion errors

● Stages ● Tasks ● Streams ● Dynamic tables ● Materialized views ● Snowpipe (for example, Auto Ingest compared to the REST API) ● Snowpipe Streaming o Snowpipe Streaming compared to the Kafka connector ● Create User-Defined Functions (UDFs) ● Design and use the Snowflake SQL API ● Openflow ● Use Notebooks to run pipelines of stored procedures for data ingestion tasks ● Use Snowflake scripting to develop and automate pipelines

● Kafka connectors ● Spark connectors ● Python connectors ● Native connectors

● Evaluate the use of a data share or a clone ● Implement a data share ○ Manage auto-fulfillment ● Create and manage views ● Implement row-level filtering ● Share data using the Snowflake Marketplace ● Share data using a listing ● Use Streamlit to build data applications and interfaces for data consumption ○ Create interactive dashboards for data exploration and sharing ○ Build self-service data access applications

● Manage external tables ● Manage Iceberg tables ● Manage hybrid tables ● Perform general table management ● Use Horizon Catalog to federate data from external catalogs ● Manage schema evolution ● Unload data

● Identify underperforming queries ● Outline telemetry around the operation ● Identify the root cause ● Increase efficiency

● Scale out compared to scale up ● Virtual warehouse properties (for example, size, multi-cluster) o Snowpark-optimized virtual warehouses ● Query complexity ● Micro-partitions and the impact of clustering ● Materialized views ● Search optimization service ● Query acceleration service ● Snowpark-optimized warehouses ● Caching features ● Use the ACCOUNT_USAGE schema ● Use warehouse metrics (such as warehouse queues) and configurations: ○ Resource monitors ○ Warehouse constraints on credit consumption ● Balance optimization with credit consumption considerations ● Optimize storage configurations and costs

● Snowflake objects ○ Tasks ■ Snowsight task history dashboards ○ Streams ○ Snowpipe Streaming ○ Alerts ○ Dynamic Tables ● Notifications ● Data quality and data metric function monitoring

● Time Travel ○ Impact of streams ● Fail-safe ● Cross-region and cross-cloud replication

● Clustering depth ● Cluster keys ● Automatic Clustering features and optimizations

● Clone objects ○ Permission inheritance ● Validate changes before promoting ● Rollback changes

● Apply object tagging and classifications ● Use data classification to monitor data ● Manage data lineage and object dependencies ● Monitor data quality

● Use Horizon Catalog to share and federate data outside of Snowflake ● Implement column-level security ○ Use in conjunction with Dynamic Data Masking ○ Use in conjunction with external tokenization ○ Use projection policies ● Use data masking with Role-Based Access Control (RBAC) to secure sensitive data ● Explain the options available to support row-level security using Snowflake row access policies ○ Use aggregation policies ● Use DDL to manage Dynamic Data Masking and row access policies ● Use best practices to create and apply data masking policies ● Use Snowflake Data Clean Rooms to share data ○ Clean room UI ○ Snowflake developer APIs

● Snowpark UDFs (for example, Java, Python, Scala) ● Secure UDFs ● SQL UDFs ● JavaScript UDFs ● User-Defined Table Functions (UDTFs) ● User-Defined Aggregate Functions (UDAFs)

● Secure external functions ● Work with external functions

● Snowpark stored procedures ● SQL Scripting stored procedures ● JavaScript stored procedures ● Transaction management

● Traverse and transform semi-structured data to structured data ● Transform structured data to semi- structured data

● Use unstructured data ○ URL types ● Use directory tables ● Use the Rest API ● Use semantic views ● Use Snowflake Cortex features to: ○ Automate data categorization ○ Extract multimedia data ○ Perform semantic data analysis ○ Run text analytics in data pipelines ○ Run multimodal AI workflows within SQL queries ● Use Cortex LLM for cost management

● Snowsight Workspaces and development environments ○ Snowflake Notebooks ● Git integration and version control ● Snowflake dbt Projects management ● Code deployment pipelines ● Testing and validation frameworks ● Environment management strategies

● Understand Snowpark architecture ● Query and filter data using the Snowpark library ● Perform data transformations using Snowpark (for example, aggregations) ● Manipulate Snowpark DataFrames