• Module 1: Data Science Concepts: This portion of the test includes basic machine learning principles, problem types, the machine learning lifecycle, and statistical ideas that are crucial for data science workloads for analysts and data scientists. It guarantees that applicants comprehend data science theory inside the framework of Snowflake’s platform.
• Module 2: Data Pipelining: This domain focuses on creating efficient data science pipelines and enhancing data through data-sharing sources for data engineers and ETL specialists. It evaluates the capacity to establish reliable data flows throughout the ecosystem of Snowflake.
• Module 3: Data Preparation and Feature Engineering: This section of the test includes data cleansing, exploratory data analysis, feature engineering, and data visualization using Snowflake for data analysts and machine learning developers. It evaluates proficiency in data preparation for model building and stakeholder presentation.
• Module 4: Model Deployment: For MLOps engineers and data scientists, this domain covers the process of moving models into production, assessing model effectiveness, retraining models, and understanding model lifecycle management tools. It ensures candidates can operationalize machine learning models in a Snowflake-based production environment.

Expected DEA-C01 Exam Topics, as suggested by Snowflake :

• Module 1: Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
• Module 2: Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
• Module 3: Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake’s Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
• Module 4: Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
• Module 5: Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.

• Module 1: Snowflake AI Data Cloud Features and Architecture: For data engineers and architects, this domain delves into the fundamental capabilities of the Snowflake platform, exploring its cloud-based infrastructure and key architectural elements.
• Module 2: Account Access and Security: For security specialists and administrators, this section examines the intricacies of user administration, diverse authentication protocols, and recommended security practices within the Snowflake environment.
• Module 3: Performance Concepts: For performance analysts and database administrators, this part of the exam investigates strategies for enhancing query efficiency, understanding caching systems, and effectively managing resources in Snowflake.
• Module 4: Data Loading and Unloading: For ETL developers and data integration specialists, this domain explores methodologies for streamlined data ingestion into Snowflake and the effective extraction of data from the platform.
• Module 5: Data Transformation: For data analysts and SQL developers, this section scrutinizes diverse approaches to data manipulation and transformation within Snowflake, encompassing a range of SQL operations and functional capabilities.
• Module 6: Data Protection and Data Sharing: For data governance professionals and collaboration managers, this part of the exam investigates Snowflake’s data protection mechanisms.

• Module 1: Define the entities and roles that are used in Snowflake/ Outline data governance capabilities in Snowflake
• Module 2: Outline key Snowflake tools and user interfaces/ Outline Snowflake storage concepts
• Module 3: Explain virtual warehouse configurations/ Optimize query performance/ Explain the use of the Query Profile
• Module 4: Continuous Data Protection with Snowflake/ Snowflake Cloud Data Platform Features and Architecture
• Module 5: Snowflake’s catalog and objects/ User-Defined Functions (UDFs) and User-Defined Table Functions (UDTFs)
• Module 6: Key features of the Snowflake Cloud Data Platform/ Account Access and Security
• Module 7: Data Cloud/ Data Exchange/ Partner Network/ Types of Column Metadata Clustering
• Module 8: Define concepts and best practices that should be considered when loading data/ Explain role hierarchy and privilege inheritance
• Module 9: Outline different commands used to load data and when they should be used/ Define concepts and best practices that should be considered when unloading data
• Module 10: Outline virtual warehouse performance tools/ Adhoc/bulk loading using the Snowflake UI

• Module 1: Accounts and Security: This section relates to creating a Snowflake account and a database strategy aligned with business needs. Users are tested for developing an architecture that satisfies data security, privacy, compliance, and governance standards.
• Module 2: Snowflake Architecture: This section assesses examining the advantages and constraints of different data models, devises data-sharing strategies, and developing architectural solutions that accommodate Development Lifecycles and workload needs.
• Module 3: Data Engineering: This section is about identifying the optimal data loading or unloading method to fulfill business requirements. Examine the primary tools within Snowflake’s ecosystem and their integration with the platform.
• Module 4: Performance Optimization: This section is about summarizing performance tools, recommended practices, and their ideal application scenarios, addressing performance challenges within current architectures, and resolving them.

• Module 1: Design data sharing solutions, based on different use cases/ Determine the appropriate data transformation solution to meet business needs
• Module 2: Design a Snowflake account and database strategy, based on business requirements/ Troubleshoot performance issues with existing architectures
• Module 3: Outline the benefits and limitations of various data models in a Snowflake environment/ Outline key tools in Snowflake’s ecosystem and how they interact with Snowflake
• Module 4: Outline performance tools, best practices, and appropriate scenarios where they should be applied/ Determine the appropriate data recovery solution in Snowflake and how data can be restored
• Module 5: Determine the appropriate data loading or data unloading solution to meet business needs/ Design an architecture that meets data security, privacy, compliance, and governance requirements
• Module 6: Create architecture solutions that support Development Lifecycles as well as workload requirements/ Outline Snowflake security principles and identify use cases where they should be applied

• Module 1: Given a scenario, configure access controls/ Set up and manage security administration and authorization
• Module 2: Given a scenario, manage databases, tables, and views/ Manage organizations and access control
• Module 3: Interpret and make recommendations for data clustering/ Manage DML locking and concurrency in Snowflake
• Module 4: Set up and manage network and private connectivity/ Given a scenario, manage Snowflake Time Travel and Fail-safe
• Module 5: Snowflake Security, Role-Based Access Control (RBAC), and User Administration/ Disaster Recovery, Backup, and Data Replication
• Module 6: Given a scenario, create and manage access control/ Given a scenario, implement resource monitors
• Module 7: Manage and implement data sharing/ Given a set of business requirements, establish access control architecture
• Module 8: Implement and manage data governance in Snowflake/ Data Sharing, Data Exchange, and Snowflake Marketplace