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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization & Scalability | 15% | - Design scalable compute and storage solutions - Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage |
| Topic 2: Data Governance, Security & Sharing | 10% | - Share data securely across accounts and platforms - Apply access control, masking, encryption, and compliance - Track lineage, audit, and maintain data integrity |
| Topic 3: Data Pipeline Orchestration & Automation | 20% | - Error handling, retry logic, and dependency management - Integrate with external orchestration tools - Build, schedule, monitor, and manage workflows |
| Topic 4: Data Ingestion | 25% | - Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Handle file formats, compression, encoding, and schema detection - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming |
| Topic 5: Data Transformation & Processing | 30% | - Use streams, tasks, dynamic tables for pipelines and CDC - Design and implement transformations: SQL, stored procedures, user-defined functions - Implement data quality, validation, and schema evolution - Work with semi-structured and unstructured data |
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NEW QUESTION # 333
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?
Answer: A,B
Explanation:
Explanation
Automating Warehouse Suspension
Data Engineer might want to consider disabling auto-suspend for a warehouse if:
He/She have a heavy, steady workload for the warehouse.
He/She require the warehouse to be available with no delay or lag time. Warehouse provisioning is generally very fast (e.g. 1 or 2 seconds); however, depending on the size of the warehouse and the availability of compute resources to provision, it can take longer.
If he/she chose to disable auto-suspend, He/she must carefully consider the costs associated with running a warehouse continually, even when the warehouse is not processing queries. The costs can be significant, especially for larger warehouses (X-Large, 2X-Large, etc.).
To disable auto-suspend, Engineer must explicitly select Never in the web interface, or specify 0 or NULL in SQL.
NEW QUESTION # 334
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?
Answer: A
Explanation:
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.
NEW QUESTION # 335
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)
Answer: A,B
Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/data-filters-about.html
https://docs.aws.amazon.com/lake-formation/latest/dg/access-control-fine-grained.html
NEW QUESTION # 336
A popular e-commerce platform experiences heavy traffic during peak hours, resulting in high latency for frequently accessed product listings. To improve performance and response times, the platform decides to implement caching for the most frequently queried product data. They aim to store the results of these queries in a highly available, low-latency cache to reduce database load and improve overall user experience.
Which AWS service would be most suitable for implementing the caching for the most frequent queries and storing their results as a cache?
Answer: C
Explanation:
Amazon MemoryDB for Redis is a fully managed Redis-compatible, in-memory database service that provides enhanced durability, replication, and performance. It is optimized for caching frequently accessed data and provides sub-millisecond latency, making it suitable for improving response times and reducing database load in high-traffic scenarios such as an e-commerce platform.
References:
https://aws.amazon.com/redshift/
https://aws.amazon.com/dynamodb/dax/
https://aws.amazon.com/s3/
https://aws.amazon.com/memorydb/
NEW QUESTION # 337
PARTITION_TYPE = USER_SPECIFIED must be used when you prefer to add and remove par-titions selectively rather than automatically adding partitions for all new files in an external storage location that match an expression?
Answer: B
Explanation:
Explanation
The CREATE EXTERNAL TABLE syntax for manually added partitions is as follows:
1.CREATE EXTERNAL TABLE
2.<table_name>
3.( <part_col_name> <col_type> AS <part_expr> )
4.[ , ... ]
5.[ PARTITION BY ( <part_col_name> [, <part_col_name> ... ] ) ]
6.PARTITION_TYPE = USER_SPECIFIED
Included the required PARTITION_TYPE = USER_SPECIFIED parameter.
NEW QUESTION # 338
......
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