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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q378-Q383):

NEW QUESTION # 378
For enabling non-ACCOUNTADMIN Roles to Perform Data Sharing Tasks, which two glob-al/account privileges snowflake provide?

Answer: A,D

Explanation:
Explanation
CREATE SHARE
In a provider account, this privilege enables creating and managing shares (for sharing data with consumer accounts).
IMPORT SHARE
In a consumer account, this privilege enables viewing the inbound shares shared with the account. Also enables creating databases from inbound shares; requires the global CREATE DATABASE privilege.
By default, these privileges are granted only to the ACCOUNTADMIN role, ensuring that only ac-count administrators can perform these tasks. However, the privileges can be granted to other roles, enabling the tasks to be delegated to other users in the account.


NEW QUESTION # 379
A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned.
An AWS Glue crawler updates the partitions.
The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
Which solution will meet these requirements?

Answer: C

Explanation:
By including predicates on your partition columns in each Athena query (for example, WHERE year = '2025' AND month = '05'), Athena prunes partitions at the optimizer level and reads only the matching S3 folders. This directly minimizes the data scanned and improves query efficiency with no additional infrastructure changes.


NEW QUESTION # 380
An ecommerce company uses AWS Glue ETL to process and analyze orders. The company wants to build an extract, transform, and load (ETL) pipeline that processes placed, shipped, delivered, and canceled orders differently.
The company integrates the order processing system with Amazon EventBridge. The company configures EventBridge Scheduler rules for each order status to invoke different AWS Glue workflows. When the company examines Amazon CloudWatch metrics for the workflow, the company notices that the FailedInvocations metric shows a high value for canceled orders.
The company must determine the cause of the failed invocations.
Which solution will meet this requirement?

Answer: D

Explanation:
Configuring a dead-letter queue for EventBridge Scheduler captures the events that could not be delivered to the target along with the failure context. Reviewing the failed canceled-order events in the DLQ provides the most direct way to identify why the invocations are failing (for example, payload issues, permission errors, or target configuration problems) without changing retry behavior.


NEW QUESTION # 381
A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:

The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?

Answer: A

Explanation:
To let drivers efficiently query only their completed rides, you need a global secondary index (GSI) with DriverID as the partition key (so queries can be targeted per driver) and RideStatus as the sort key (so you can query for "Completed" rides without scanning the full table). This avoids costly scans and supports fast, targeted lookups at scale.


NEW QUESTION # 382
Can Masking policies be applied to virtual columns?

Answer: B


NEW QUESTION # 383
......

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