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EMC D-DS-OP-23 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Streaming & IoT Data Processing | 15% | - Pravega, Kafka, NiFi usage - Stream processing optimization - Real-time ingestion frameworks |
| Data Processing Frameworks | 22% | - Batch and real-time processing optimization - Apache Spark optimization - Hadoop ecosystem tuning |
| Governance, Security & Compliance | 15% | - Regulatory compliance - Security and privacy protection - Data governance policies |
| Storage & Data Management | 18% | - SQL/NoSQL performance tuning - Distributed storage system optimization - Data formats, partitioning, and indexing |
| Data Pipeline Optimization | 20% | - ETL/ELT workflow optimization - Reduce latency and improve throughput - Design scalable and efficient data pipelines |
| Monitoring, Troubleshooting & Workload Management | 10% | - Cluster resource allocation - Bottleneck identification and resolution - Performance monitoring and logging |
EMC Dell Data Scientist and Data Engineering Optimize 2023 Sample Questions:
1. An organization plans to establish a data governance process for a data lake.
What is the correct sequence of steps required to implement the process?
A) 1. Define the roles and responsibilities of actors within the data governance process
2. Operationalize the data governance process and assist the deployment team
3. Understand the current and future states of data governance and identify remaining gaps
4. Develop the business value statement and a baseline for ongoing measurement of the data governance deployment
B) 1. Define the roles and responsibilities of actors within the data governance process
2. Operationalize the data governance process and assist the deployment team
3. Develop the business value statement and a baseline for ongoing measurement of the data governance deployment
4. Understand the current and future states of data governance and identify remaining gaps
C) 1. Develop the business value statement and a baseline for ongoing measurement of the data governance deployment
2. Define the roles and responsibilities of actors within the data governance process
3. Operationalize the data governance process and assist the deployment team
4. Understand the current and future states of data governance and identify remaining gaps
D) 1. Define the roles and responsibilities of actors within the data governance process
2. Develop the business value statement and a baseline for ongoing measurement of the data governance deployment
3. Operationalize the data governance process and assist the deployment team
4. Understand the current and future states of data governance and identify remaining gaps
2. Which feature of Apache Kafka makes it suitable for handling high-throughput data streams?
A) Only supports batch processing
B) Static schema enforcement
C) Partitioning and replication
D) In-memory data storage
3. In the ELT process, data is transformed after being loaded into the target system.
Which tools can be used for ELT? (Select all that apply)
A) Amazon Redshift
B) Apache Hadoop
C) Apache Spark
D) Talend
E) Microsoft SSIS
4. In Python, which type of non-linear list always has a common root?
A) Queues
B) Trees
C) Graphs
D) Stacks
5. Which NoSQL database type is best suited for handling graph-based data structures and complex relationships?
A) Graph database
B) Column-family store
C) Key-value store
D) Document store
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C,D,E | Question # 4 Answer: B | Question # 5 Answer: A |



