DEA-C01試験参考書 & DEA-C01専門知識内容

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Snowflake DEA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Advanced Snowflake Features10-15%- Data Sharing
  • 1. Secure share creation and management
  • 2. Reader accounts
  • 3. Direct sharing vs Data Marketplace
- Data Lakes
  • 1. Snowflake as a Data Lake
  • 2. External tables and Iceberg support
  • 3. Querying semi-structured data (VARIANT, ARRAY, OBJECT)
- External Integrations
  • 1. Partner integrations (Spark, Kafka, etc.)
  • 2. External functions and UDFs
  • 3. Snowflake Connector for Python/Java
Data Storage and Retrieval15-20%- Performance Optimization
  • 1. Result set caching
  • 2. Materialized views and cached results
  • 3. Query profiling and execution plans
  • 4. Warehouse scaling policies
- Snowflake Architecture
  • 1. Hybrid and Hyper tables
  • 2. Table types (permanent, transient, temporary)
  • 3. Clustering strategies and key selection
  • 4. Micro-partitioning concepts
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. Data type conversions and casting
  • 2. Window functions for advanced analytics
  • 3. Merge, update, and delete operations
  • 4. Complex JOIN operations and optimizations
Data Governance and Security15-20%- 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. Secure data sharing across accounts
  • 2. External tokenization
  • 3. Data re-identification risks
  • 4. Data masking and tokenization
- Compliance and Monitoring
  • 1. Access history and audit trails
  • 2. Tag-based policies
  • 3. Data classification
Data Ingestion15-20%- Bulk Data Loading
  • 1. Data loading error handling and validation
  • 2. COPY INTO command options and best practices
  • 3. File format considerations (CSV, JSON, Parquet, Avro)
  • 4. Staging configurations (internal and external stages)
- Continuous Data Ingestion
  • 1. Snowpipe configuration and usage
  • 2. Serverless compute for Snowpipe
  • 3. Real-time data ingestion patterns
Performance Engineering10-15%- Query Optimization
  • 1. Predicate pushdown optimization
  • 2. Join elimination and simplification
  • 3. Query rewrite and optimization techniques
- 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 認定 DEA-C01 試験問題 (Q356-Q361):

質問 # 356
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?

正解：C

解説：
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files


質問 # 357
Charles, A Lead Data engineer, with ACCOUNTADMIN role wants to configure the time travel for one of the Schema's object. He setup the MIN_DATA_RETENTION_TIME_IN_DAYS pa-rameter with Value 79 at account level but he figured out that DA-TA_RETENTION_TIME_IN_DAYS is already set with value 81 at account level. What would be the effective minimum data retention period for an object?

正解：A

解説：
Explanation
A user with the ACCOUNTADMIN role can also set the MIN_DATA_RETENTION_TIME_IN_DAYS at the account level. This parameter setting enforc-es a minimum data retention period for databases, schemas, and tables. Setting MIN_DATA_RETENTION_TIME_IN_DAYS does not alter or replace the DA-TA_RETENTION_TIME_IN_DAYS parameter value. It may, however, change the effective data retention period for objects. When MIN_DATA_RETENTION_TIME_IN_DAYS is set at the ac-count level, the data retention period for an object is determined by MAX(DATA_RETENTION_TIME_IN_DAYS, MIN_DATA_RETENTION_TIME_IN_DAYS).


質問 # 358
A Data Engineer enables a result cache at the session level with the following command:
ALTER SESSION SET USE CACHED RESULT = TRUE;
The Engineer then runs the following select query twice without delay:

The underlying table does not change between executions
What are the results of both runs?

正解：A

解説：
Explanation
The result cache is enabled at the session level, which means that repeated queries will return cached results if there is no change in the underlying data or session parameters. However, in this case, the result cache is not relevant because the query uses a specific SEED value for sampling, which makes it deterministic. Therefore, both runs will return the same results regardless of caching.


質問 # 359
A company has a business intelligence platform on AWS. The company uses an AWS Storage Gateway Amazon S3 File Gateway to transfer files from the company's on-premises environment to an Amazon S3 bucket.
A data engineer needs to setup a process that will automatically launch an AWS Glue workflow to run a series of AWS Glue jobs when each file transfer finishes successfully.
Which solution will meet these requirements with the LEAST operational overhead?

正解：B

解説：
Using EventBridge directly to trigger the AWS Glue workflow upon S3 events is straightforward and leverages AWS's event-driven architecture, requiring minimal maintenance.


質問 # 360
The following code is executed ina Snowflake environment with the default settings:

What will be the result of the select statement?

正解：A


質問 # 361
......

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