DEA-C01 Dumps, DEA-C01덤프문제집

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Snowflake DEA-C01 Exam Syllabus Topics:

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

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DEA-C01덤프문제집 - DEA-C01시험패스 가능 덤프자료

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최신 SnowPro Advanced DEA-C01 무료샘플문제 (Q39-Q44):

질문 # 39
A data engineer uploads confidential documents to an Amazon S3 bucket every day. The data engineer requires a solution to independently verify the integrity of all uploaded data to confirm that there was no corruption during the transfer process.
Which solution will meet this requirement?

정답：B

설명：
By calculating a checksum (e.g., SHA-256) before upload and providing it in the upload request, Amazon S3 verifies the checksum on receipt and rejects the upload if the data is corrupted. This provides automatic, independent validation of data integrity without requiring full data downloads.


질문 # 40
A data engineer needs to run a data transformation job whenever a user adds a file to an Amazon S3 bucket. The job will run for less than 1 minute. The job must send the output through an email message to the data engineer. The data engineer expects users to add one file every hour of the day.
Which solution will meet these requirements in the MOST operationally efficient way?

정답：A

설명：
An S3 event notification on ObjectCreated can directly invoke a Lambda function as soon as a new file arrives. Lambda can perform your <1 minute transformation and then publish the result to an SNS topic. With the data engineer subscribed to that topic by email, the output is delivered automatically
- no servers to manage, no polling, and minimal ongoing operations.


질문 # 41
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?

정답：B

설명：
Lambda layers allow you to package external libraries, custom scripts, or dependencies (like Python scripts) separately from the Lambda function code. By using Lambda layers, you can update the custom Python scripts in one central place (the layer) without manually updating each Lambda function. When the layer is updated, all Lambda functions that use the layer automatically get the latest version of the scripts.


질문 # 42
Ryan, a Data Engineer, accidently drop the Share named SF_SHARE which results in immediate access revoke for all the consumers (i.e., accounts who have created a database from that SF_SHARE). What action he can take to recover the dropped Share?

정답：D

설명：
Explanation
You can drop a share at any time using the DROP SHARE command.
Dropping a share instantly invalidates all databases created from the share by consumer accounts.
All queries and other operations performed on these databases will no longer work.
After dropping a share, you can recreate it with the same name;
however, this does not restore any of the databases created from the share by consumer accounts.
The recreated share is treated as a new share and all consumer accounts must create a new database from the new share.


질문 # 43
A data engineer is using Amazon QuickSight to build a dashboard to report a company's revenue in multiple AWS Regions. The data engineer wants the dashboard to display the total revenue for a Region, regardless of the drill-down levels shown in the visual.
Which solution will meet these requirements?

정답：D

설명：
Level-Aware Calculations (LAC) in Amazon QuickSight allow users to create calculations that are independent of the levels of aggregation in a visual.
Specifically, the LAC-A (Aggregate) function lets you calculate metrics such as the total revenue for a region regardless of the drill-down levels displayed. This means that even if the user drills down into more granular data, the total revenue for the Region will still be displayed in the dashboard, meeting the requirements.
Table calculations are context-dependent, meaning they calculate values based on the visual's current granularity and drill-down levels. This would not meet the requirement to show total revenue regardless of drill-down.
A simple calculated field operates at the current visual's level of detail and would change as you drill down. It would not maintain the total revenue for a region regardless of drill-down.
LAC-W (Window) functions are typically used for running totals, moving averages, or ranking, which are calculated over a range of data but still affected by drill-down levels. This would not ensure the total revenue is displayed regardless of drill-down.


질문 # 44
......

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