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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Orchestration & Automation | 20% | - Build, schedule, monitor, and manage workflows - Error handling, retry logic, and dependency management - Integrate with external orchestration tools |
| Topic 2: Data Governance, Security & Sharing | 10% | - Share data securely across accounts and platforms - Track lineage, audit, and maintain data integrity - Apply access control, masking, encryption, and compliance |
| Topic 3: Data Transformation & Processing | 30% | - Implement data quality, validation, and schema evolution - Use streams, tasks, dynamic tables for pipelines and CDC - Work with semi-structured and unstructured data - Design and implement transformations: SQL, stored procedures, user-defined functions |
| Topic 4: Data Ingestion | 25% | - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Handle file formats, compression, encoding, and schema detection - Ingest from various sources: cloud storage, on-premises, APIs, data lakes |
| Topic 5: Performance Optimization & Scalability | 15% | - Design scalable compute and storage solutions - Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage |
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NEW QUESTION # 106
A company uses an Amazon S3 bucket to integrate multiple data sources into a central data lake.
The company needs to perform multiple transformations and data cleaning processes on the data to make the data accessible to business partners.
The company needs a solution that will give multiple business partners the ability to run SQL queries on the central data lake during normal business hours.
Which solution will meet these requirements MOST cost-effectively?
Answer: A
Explanation:
AWS Glue Flex Jobs provide discounted, off-hours batch ETL to transform S3 data, and Amazon Redshift Serverless offers on-demand SQL access during business hours without paying for idle clusters minimizing cost and operations.
NEW QUESTION # 107
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real- time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)
Answer: A,D
Explanation:
Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data directly within Redshift.
Redshift can invoke external services, such as a SageMaker real-time endpoint, using SQL queries. This allows the company to send real-time data from Redshift to SageMaker and receive predictions (e.g., inventory forecasting) in real time, meeting the need for real-time predictions.
NEW QUESTION # 108
A company runs concurrent analytical queries on Amazon Redshift tables multiple times each day. The queries require consistent data views three times each day. The company runs extract, transform, and load (ETL) operations that update dimension tables while the queries run.
The company has noticed that the queries cause table-level locks during the ETL operations. The company's current solution experiences query timeouts and deadlocks during peak processing hours, which affects analytical reporting and on-demand analysis.
Which solution will fix this issue?
Answer: A
Explanation:
Using separate Amazon Redshift clusters for ETL and analytics isolates write-heavy ETL activity from read-heavy analytical workloads, which eliminates the lock contention that is causing timeouts and deadlocks. Redshift data sharing provides near real-time, transactionally consistent access to the same data across clusters, so analysts can query a stable view of the data while ETL processes continue independently.
NEW QUESTION # 109
The JSON below is stored in a variant column named v in a table named jCustRaw:
Which query will return one row per team member (stored in the teamMembers array) along all of the attributes of each team member?
Answer: B
NEW QUESTION # 110
A company's application needs to search and analyze data in near real time. The application must handle up to 1,000 requests each second with low query latency. The company wants a solution that individual data teams can own and configure to meet each team's cost and performance optimization requirements. Which solution will meet these requirements?
Answer: C
Explanation:
Amazon OpenSearch Service is built for near real-time search and analytics with low query latency, making it a strong fit for applications that must handle about 1,000 requests per second.
Giving each data team its own OpenSearch cluster lets each team independently tune storage, indexing, scaling, and cost/performance settings to match its own workload requirements.
Reference:
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/bp.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless- overview.html
NEW QUESTION # 111
......
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