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Snowflake DEA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Ingestion15-20%- Continuous Data Ingestion
  • 1. Serverless compute for Snowpipe
  • 2. Snowpipe configuration and usage
  • 3. Real-time data ingestion patterns
- Bulk Data Loading
  • 1. Staging configurations (internal and external stages)
  • 2. COPY INTO command options and best practices
  • 3. Data loading error handling and validation
  • 4. File format considerations (CSV, JSON, Parquet, Avro)
Data Storage and Retrieval15-20%- Snowflake Architecture
  • 1. Hybrid and Hyper tables
  • 2. Table types (permanent, transient, temporary)
  • 3. Clustering strategies and key selection
  • 4. Micro-partitioning concepts
- Performance Optimization
  • 1. Warehouse scaling policies
  • 2. Query profiling and execution plans
  • 3. Result set caching
  • 4. Materialized views and cached results
Data Governance and Security15-20%- Access Control
  • 1. Role-based access control (RBAC)
  • 2. GRANT and REVOKE operations
  • 3. Row-level and column-level security
  • 4. Role hierarchy and ownership
- Compliance and Monitoring
  • 1. Data classification
  • 2. Tag-based policies
  • 3. Access history and audit trails
- Data Protection
  • 1. Data masking and tokenization
  • 2. Data re-identification risks
  • 3. External tokenization
  • 4. Secure data sharing across accounts
Advanced Snowflake Features10-15%- Data Sharing
  • 1. Reader accounts
  • 2. Secure share creation and management
  • 3. Direct sharing vs Data Marketplace
- Data Lakes
  • 1. External tables and Iceberg support
  • 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT)
  • 3. Snowflake as a Data Lake
- External Integrations
  • 1. External functions and UDFs
  • 2. Partner integrations (Spark, Kafka, etc.)
  • 3. Snowflake Connector for Python/Java
Data Transformation20-25%- Data Processing Patterns
  • 1. ELT vs ETL approaches in Snowflake
  • 2. Zero Copy Cloning for data provisioning
  • 3. Time Travel for historical data access
- SQL-Based Transformations
  • 1. Complex JOIN operations and optimizations
  • 2. Data type conversions and casting
  • 3. Window functions for advanced analytics
  • 4. Merge, update, and delete operations
- Streams and Tasks
  • 1. Scheduled and event-based task execution
  • 2. Serverless task warehouse configuration
  • 3. Change Data Capture (CDC) with streams
  • 4. Task graph design and dependencies
Performance Engineering10-15%- Query Optimization
  • 1. Join elimination and simplification
  • 2. Query rewrite and optimization techniques
  • 3. Predicate pushdown optimization
- Warehouse Management
  • 1. Multi-cluster warehouse configuration
  • 2. Auto-suspend and auto-resume settings
  • 3. Resource monitors and cost control

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q192-Q197):

NEW QUESTION # 192
A company has a data pipeline that processes transaction data in real time. The company needs a notification system that alerts different teams based on the type of processing error without any delay. For security-related errors, the system must immediately notify the security team. For data validation errors, the system must notify the data quality team. For system errors, the system must notify the operations team. Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Amazon EventBridge is designed to route events based on matching rules and event patterns, so the company can classify each processing error type and immediately send it to the correct destination without custom routing code. By using separate EventBridge rules for security, data validation, and system errors and sending each to its own Amazon SNS topic, the solution stays fully managed, low latency, and operationally simpler than introducing Lambda logic, queue polling, or metric-based alarms.
Reference:
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-event-patterns.html
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-what-is.html
https://docs.aws.amazon.com/sns/latest/dg/welcome.html


NEW QUESTION # 193
Which are the two ways to access elements in a JSON object?

Answer: A,C


NEW QUESTION # 194
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format.
The company is experiencing slow query performance.
Which solutions will improve query performance? (Choose two.)

Answer: C,D

Explanation:
Compaction reduces many small Parquet files into larger ones, lowering file-listing and open/scan overhead for faster reads.
Copy-on-write favors read performance by materializing updates into rewritten data files, resulting in more contiguous, scan-efficient files for queries.


NEW QUESTION # 195
A Data Engineer would like to define a file structure for loading and unloading data Where can the file structure be defined? (Select THREE)

Answer: B,D,F

Explanation:
Explanation
The places where the file format can be defined are copy command, file format object, and stage object. These places allow specifying or referencing a file format that defines how data files are parsed and loaded into or unloaded from Snowflake tables. A file format can include various options, such as field delimiter, field enclosure, compression type, date format, etc. The other options are not places where the file format can be defined. Option B is incorrect because MERGE command is a SQL command that can merge data from one table into another based on a join condition, but it does not involve loading or unloading data files. Option D is incorrect because pipe object is a Snowflake object that can load data from an external stage into a Snowflake table using COPY statements, but it does not define or reference a file format. Option F is incorrect because INSERT command is a SQL command that can insert data into a Snowflake table from literal values or subqueries, but it does not involve loading or unloading data files.


NEW QUESTION # 196
Snowpipe API provides a REST endpoint for defining the list of files to ingest that Informs Snow-flake about the files to be ingested into a table. A successful response from this endpoint means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. What is name of this Endpoint?

Answer: A

Explanation:
Explanation
The Snowpipe API provides a REST endpoint for defining the list of files to ingest.
Endpoint: insertFiles
Informs Snowflake about the files to be ingested into a table. A successful response from this end-point means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. For more details, see the response codes below.
In most cases, Snowflake inserts fresh data into the target table within a few minutes.
To Know more about SnowFlake Rest API used for Data File ingestion, do refer:
https://docs.snowflake.com/en/user-guide/data-load-snowpipe-rest-apis.html#data-file-ingestion


NEW QUESTION # 197
......

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