DEA-C01考題寶典 & DEA-C01真題材料

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Snowflake DEA-C01 Exam Syllabus Topics:

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

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最新的 SnowPro Advanced DEA-C01 免費考試真題 (Q149-Q154):

問題 #149
A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.
A data engineer needs to create a solution to monitor the entire pipeline.
Which solution will meet these requirements?

答案：B

解題說明：
Amazon EventBridge can monitor the execution status of AWS Step Functions state machines and trigger specific actions based on state changes, such as when a state machine succeeds or fails. By configuring an EventBridge rule to capture these execution status changes and forward the details to an Amazon SNS topic, real-time notifications can be sent to alert users. This solution ensures a scalable and event-driven approach to monitoring the entire data processing pipeline in real time.


問題 #150
A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.
The company wants to reclaim disk space so that the company will not run out of storage space.
The company also wants to analyze the sort key column.
Which Amazon Redshift command will meet these requirements?

答案：B


問題 #151
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand. Which solution will meet these requirements?

答案：B

解題說明：
Amazon DynamoDB with on-demand capacity is designed to handle unpredictable, high-volume request patterns and sudden traffic spikes while maintaining single-digit millisecond latency.
Using Product ID as the table's primary key enables efficient item retrieval by the primary identifier, and global secondary indexes on Category and Brand provide flexible, low-latency queries on those secondary attributes.


問題 #152
A data engineer notices that Amazon Athena queries are held in a queue before the queries run.
How can the data engineer prevent the queries from queueing?

答案：D

解題說明：
https://aws.amazon.com/blogs/aws/introducing-athena-provisioned-capacity/


問題 #153
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.
Which command or commands will meet this requirement?

答案：A

解題說明：
In Amazon Redshift data sharing, cross-account permissions for a datashare are granted to consumer cluster/serverless namespaces. Granting USAGE on the datashare to each consumer namespace enables both teams' Redshift environments in their respective accounts to create and query databases from the shared data.


問題 #154
......

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