SnowPro Advanced: Data Engineer Practice Exam
SnowPro Advanced: Data Engineer Practice Exam
About SnowPro Advanced: Data Engineer Practice Exam
The SnowPro Advanced: Data Engineer Certification Exam has been developed to test advanced knowledge and skills used to apply comprehensive data engineering principles using Snowflake.
Skills Evaluated
The SnowPro Advanced: Data Engineer certification exam will assess the ability of -
- Sourcing data from Data Lakes, APIs, and on-premises
- Transforming, replicating, and sharing data across cloud platforms
- Designing end-to-end near real-time streams
- Designing scalable compute solutions for DE workloads
- Evaluating performance metrics
Who should take the SnowPro Advanced: Data Engineer?
- The candidates appearing for the SnowPro Advanced: Data Engineer exam should have more than 2 years of hands-on Snowflake Practitioner experience in a Data Engineering role.
Suggested roles includes -
- Data Engineers
Exam Format
- Exam Code: DEA-C01
- Total Questions: 65
- Type of Question: Multiple Select, Multiple Choice
- Exam Duration: 115 minutes
- Exam Language: English
- Passing Score: 750 On a scaled score (0 - 1000)
- Exam Prerequisites: SnowPro Core Certified
- Delivery Options: Online Proctoring and Onsite Testing Centers
Course Outline
The SnowPro Advanced: Data Engineer exam covers the following topics -
Domain1 - Overview of Data Movement
1.1 Load data into Snowflake from a given data set.
1.2 Ingest data of various formats using the mechanisms provided by Snowflake.
1.3 Troubleshoot issues related to data ingestion.
1.4 Design, build, and troubleshoot continuous data pipelines.
1.5 Analyze and distinguish between different types of data pipelines.
1.6 Install, configure, and utilize connectors to establish connections with Snowflake.
1.7 Design and construct solutions for data sharing.
1.8 Explain the appropriate usage and functionality of external tables.
Domain 2: Overview of Performance Optimization
2.1 Troubleshoot queries that are underperforming.
2.2 Configure optimal performance solutions based on given scenarios.
2.3 Outline and utilize caching features.
2.4 Monitor continuous data pipelines.
Domain 3: Storage and Data Protection
3.1 Implement data recovery features within the Snowflake environment.
3.2 Describe the impact of streams on Time Travel.
3.3 Utilize system functions to analyze micro-partitions.
3.4 Utilize Time Travel and cloning to create new development environments.
Domain 4: Overview of Security
4.1 Outline the fundamental security principles in Snowflake.
4.2 Provide an overview of system-defined roles and their appropriate application.
4.3 Manage data governance.
Domain 5: Data Transformation
5.1 Define User-Defined Functions (UDFs) and explain how to utilize them.
5.2 Define and create external functions.
5.3 Design, build, and leverage stored procedures.
5.4 Handle and transform semi-structured data.
5.5 Utilize Snowpark for data transformation purposes.
