DEA-C01日本語的中対策、DEA-C01ブロンズ教材

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Snowflake DEA-C01 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Loading and Integration- Snowpipe and continuous ingestion
  • 1. Automated ingestion workflows
    • 2. Event-driven loading
      - Bulk data loading
      • 1. File format handling
        • 2. COPY INTO command usage
          Topic 2: Semi-Structured and Advanced Data Handling- Semi-structured data processing
          • 1. VARIANT data type usage
            • 2. JSON, Avro, Parquet handling
              - Advanced transformation techniques
              • 1. Complex transformations in SQL
                • 2. Flattening nested data
                  Topic 3: Data Engineering Fundamentals on Snowflake- Data ingestion concepts
                  • 1. Continuous ingestion concepts
                    • 2. Batch loading strategies
                      - Snowflake architecture for data engineering
                      • 1. Storage and compute separation
                        • 2. Virtual warehouses and compute scaling
                          Topic 4: Performance Optimization- Query performance tuning
                          • 1. Clustering and micro-partitions
                            • 2. Caching mechanisms
                              - Warehouse optimization
                              • 1. Scaling strategies
                                • 2. Concurrency management
                                  Topic 5: Data Pipelines and Transformation- Streams and Tasks
                                  • 1. Change Data Capture (CDC) with Streams
                                    • 2. Automating pipelines with Tasks
                                      - ETL/ELT design patterns in Snowflake
                                      • 1. Data orchestration concepts
                                        • 2. Staging and transformation layers
                                          Topic 6: Security, Governance, and Data Sharing- Data governance
                                          • 1. Data masking policies
                                            • 2. Secure data sharing concepts
                                              - Access control
                                              • 1. Role-based access control (RBAC)
                                                • 2. Object-level permissions

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                                                  Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q229-Q234):

                                                  質問 # 229
                                                  A data engineer needs to deploy a complex pipeline. The stages of the pipeline must be able to run a script. The data engineer must use only fully managed and serverless services in the pipeline. Which solution will meet these requirements?

                                                  正解：C

                                                  解説：
                                                  Amazon Managed Workflows for Apache Airflow is a fully managed, serverless service designed for orchestrating complex pipelines. It supports defining multiple stages as tasks that can run scripts, manage dependencies, and handle scheduling, retries, and monitoring without requiring infrastructure management, fully meeting the requirement for a complex, script-based pipeline using only managed and serverless services.


                                                  質問 # 230
                                                  A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
                                                  The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
                                                  Which solution will meet these requirements?

                                                  正解：B

                                                  解説：
                                                  Use the ALL Distribution Style for Rarely Updated Small Tables. This approach optimizes the performance of joins involving these smaller tables and is a common best practice in Redshift data warehousing. For the larger tables, maintaining the EVEN distribution style or considering a KEY-based distribution (if there are common join columns) could be more appropriate.


                                                  質問 # 231
                                                  A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
                                                  How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

                                                  正解：C

                                                  解説：
                                                  Explanation
                                                  The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.


                                                  質問 # 232
                                                  A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.
                                                  A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.
                                                  Which solution will meet this requirement?

                                                  正解：B

                                                  解説：
                                                  Data Quality Ruleset: Creating a ruleset with Data Quality Definition Language (DQDL) rules allows for defining and evaluating data quality on specific AWS Glue tables, enabling automated checks on data quality.
                                                  Scheduled Execution: Running the ruleset daily ensures that data quality scores are regularly updated.
                                                  AWS Glue Data Source in Amazon DataZone: Configuring Amazon DataZone with an AWS Glue data source enables seamless integration, allowing data quality scores from AWS Glue Data Quality to be published to the Amazon DataZone portal.


                                                  質問 # 233
                                                  A company has three subsidiaries. Each subsidiary uses a different data warehousing solution.
                                                  The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
                                                  The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
                                                  A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
                                                  Which solution will meet these requirements with the LEAST operational effort?

                                                  正解：B

                                                  解説：
                                                  Amazon Athena federated query allows querying data from multiple data sources, including Amazon Redshift, Teradata, and Google BigQuery, using their federated query connectors. This solution offers a serverless approach, reducing the operational overhead of managing infrastructure while allowing SQL-based transformations across all data sources. Once the data is read and joined, Athena can write the results back to Amazon S3 in the Iceberg table format with a Merge operation.
                                                  This approach minimizes the operational effort as Athena manages the complexity of connecting to different databases through its connectors, and you can perform the necessary transformations and data joins using familiar SQL.
                                                  While AWS Glue is a powerful ETL tool, it requires more operational effort to manage complex transformations across multiple systems, and managing native transforms across different engines (Redshift, Teradata, BigQuery) in Glue can introduce additional complexity.
                                                  Amazon EMR with PySpark can handle the task, but it requires more operational effort to manage and maintain the EMR cluster. Writing and maintaining PySpark code can also be more complex compared to using SQL in Athena.
                                                  Appflow is primarily designed for simple data movement between SaaS applications and AWS services, but it does not provide the complex transformation and joining capabilities needed for this scenario. Using Athena after Appflow for joins adds unnecessary complexity compared to directly using federated queries in Athena.


                                                  質問 # 234
                                                  ......

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