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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion	15-20%	- Continuous Data Ingestion 1. Snowpipe configuration and usage 2. Real-time data ingestion patterns 3. Serverless compute for Snowpipe - Bulk Data Loading 1. Data loading error handling and validation 2. Staging configurations (internal and external stages) 3. File format considerations (CSV, JSON, Parquet, Avro) 4. COPY INTO command options and best practices
Data Governance and Security	15-20%	- Access Control 1. Role-based access control (RBAC) 2. Row-level and column-level security 3. GRANT and REVOKE operations 4. Role hierarchy and ownership - Data Protection 1. Data re-identification risks 2. Data masking and tokenization 3. External tokenization 4. Secure data sharing across accounts - Compliance and Monitoring 1. Data classification 2. Access history and audit trails 3. Tag-based policies
Advanced Snowflake Features	10-15%	- Data Sharing 1. Reader accounts 2. Secure share creation and management 3. Direct sharing vs Data Marketplace - Data Lakes 1. Snowflake as a Data Lake 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 3. External tables and Iceberg support - External Integrations 1. External functions and UDFs 2. Partner integrations (Spark, Kafka, etc.) 3. Snowflake Connector for Python/Java
Performance Engineering	10-15%	- Query Optimization 1. Join elimination and simplification 2. Predicate pushdown optimization 3. Query rewrite and optimization techniques - Warehouse Management 1. Resource monitors and cost control 2. Multi-cluster warehouse configuration 3. Auto-suspend and auto-resume settings
Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Micro-partitioning concepts 2. Hybrid and Hyper tables 3. Clustering strategies and key selection 4. Table types (permanent, transient, temporary) - Performance Optimization 1. Query profiling and execution plans 2. Warehouse scaling policies 3. Materialized views and cached results 4. Result set caching
Data Transformation	20-25%	- SQL-Based Transformations 1. Merge, update, and delete operations 2. Complex JOIN operations and optimizations 3. Window functions for advanced analytics 4. Data type conversions and casting - Streams and Tasks 1. Change Data Capture (CDC) with streams 2. Scheduled and event-based task execution 3. Serverless task warehouse configuration 4. Task graph design and dependencies - Data Processing Patterns 1. Time Travel for historical data access 2. ELT vs ETL approaches in Snowflake 3. Zero Copy Cloning for data provisioning

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

A. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
B. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
C. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis
D. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.

Answer: A

Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.

NEW QUESTION # 24
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning.
The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

A. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.
B. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.
C. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
D. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times.
Schedule lower capacity during off-peak times.

Answer: D

NEW QUESTION # 25
A company uses an Amazon S3 Standard bucket to maintain a self-managed transactional data lake that uses Apache Iceberg tables. The data lake ingests data both in real time and in batches.
Users report slow performance for real-time tables. A data engineer reviews the real-time tables and notices that the tables are made up of many small data files The data engineer must improve the performance of the real-time tables.
Which solution will meet this requirement?

A. Expire historic snapshots.
B. Apply compaction.
C. Archive historic snapshots.
D. Delete S3 objects that are not linked from the Iceberg table.

Answer: B

Explanation:
Compaction merges many small data files into fewer, larger files, which reduces file count and metadata overhead in Apache Iceberg tables, directly improving query performance for real-time workloads.

NEW QUESTION # 26
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports.
A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge. All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently.
Which solution will meet these requirements with the LEAST operational effort?

A. Use Quick Suite dashboards that have zero-ETL access to Amazon Redshift.
B. Integrate Tableau with Amazon Redshift to give Tableau direct access to the data.
C. Use Quick Suite dashboards that have federated query access to Amazon Redshift.
D. Enable Amazon Q in Quick Suite. Generate Quick Suite dashboards and reports.

Answer: D

Explanation:
Amazon Q in QuickSight enables users to create dashboards and reports using natural language queries, removing the need for SQL knowledge. It is fully integrated and managed within QuickSight, providing the most operationally efficient way for analysts of varying skill levels to generate insights quickly without additional tooling or infrastructure.

NEW QUESTION # 27
A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.
Which conditions must be met? (Select THREE).

A. The USED_CACHED_RESULT parameter must be included in the query.
B. The table structure contributing to the query result cannot have changed
C. The new query must have the same syntax as the previously executed query.
D. The micro-partitions cannot have changed due to changes to other data in the table
E. The results must be reused within 72 hours.
F. The query must be executed using the same virtual warehouse.

Answer: B,C,E

Explanation:
Explanation
Snowflake's query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:
The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.

NEW QUESTION # 28
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