ARA-C01 Data Engineering Practice Question
A company is implementing a Data Lakehouse architecture and wants to store their data in the Apache Iceberg format on S3 while still using Snowflake for high-performance analytics. What is the most important architectural consideration when using Snowflake-managed Iceberg tables?
⚠ Common exam trap
Candidates often believe that Iceberg tables in Snowflake require manual metadata file management, failing to realize that Snowflake-managed Iceberg tables handle all orchestration and maintenance automatically for the user.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Snowflake will handle all data maintenance and metadata updates for the table.
Iceberg tables in Snowflake can be either Snowflake-managed or Externally-managed. When Snowflake-managed, Snowflake handles the metadata and file orchestration, providing performance similar to native tables while storing data in an open format. This allows other tools to read the Parquet files while Snowflake remains the primary engine.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Snowflake-managed Iceberg tables cannot be queried by external engines like Spark.
Why it's wrong here
One of the primary benefits of Iceberg is its open nature. Even when managed by Snowflake, the data is stored in Parquet format with an Iceberg metadata layer, allowing external engines like Apache Spark or Trino to read the data directly from the cloud storage.
- ✓
Snowflake will handle all data maintenance and metadata updates for the table.
Why this is correct
In a Snowflake-managed configuration, the architect delegates the responsibility for generating metadata and managing file layouts to Snowflake. This ensures that the table benefits from Snowflake's performance optimizations and features like automatic clustering, while still adhering to the Iceberg open standard.
- ✗
The data must be stored in Snowflake's internal proprietary format to use Iceberg.
Why it's wrong here
Iceberg tables specifically use open formats like Parquet for data storage. They do not use Snowflake's proprietary internal format. The goal of an Iceberg table is to provide an open-table format that is interoperable across different compute engines and storage platforms.
- ✗
Iceberg tables do not support Time Travel or Fail-safe features.
Why it's wrong here
Snowflake-managed Iceberg tables support core Snowflake features like Time Travel. This allows architects to maintain the same data governance and recovery standards for Iceberg tables as they do for standard Snowflake tables, ensuring a consistent experience across the hybrid data landscape.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Snowflake exam blueprint
This ARA-C01 practice question is part of Courseiva's free Snowflake certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the ARA-C01 exam.