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EMC D-DS-OP-23 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Streaming & IoT Data Processing | 15% | - Real-time ingestion frameworks - Pravega, Kafka, NiFi usage - Stream processing optimization |
| Topic 2: Monitoring, Troubleshooting & Workload Management | 10% | - Cluster resource allocation - Performance monitoring and logging - Bottleneck identification and resolution |
| Topic 3: Data Pipeline Optimization | 20% | - Reduce latency and improve throughput - Design scalable and efficient data pipelines - ETL/ELT workflow optimization |
| Topic 4: Data Processing Frameworks | 22% | - Batch and real-time processing optimization - Hadoop ecosystem tuning - Apache Spark optimization |
| Topic 5: Governance, Security & Compliance | 15% | - Data governance policies - Security and privacy protection - Regulatory compliance |
| Topic 6: Storage & Data Management | 18% | - Data formats, partitioning, and indexing - Distributed storage system optimization - SQL/NoSQL performance tuning |
EMC Dell Data Scientist and Data Engineering Optimize 2023 Sample Questions:
1. Which tools are used for security considerations in the Hadoop ecosystem? (Select all that apply)
A) Apache Knox
B) Apache Ranger
C) Apache Flink
D) Apache Atlas
E) Apache Sentry
2. An organization has implemented an IoT solution in their environment to gain competitive advantage.
This solution generates huge volumes of streaming data from different sources.
The organization wants to leverage this data to provide predictive, actionable results. They need a real- time data processing tool that can process the data using a fault-tolerant, horizontally scalable method.
Which tool should the organization use?
A) Apache Ambari
B) Apache Ranger
C) Apache Oozie
D) Apache Storm
3. In a relational database, what is a minimal set of attributes to uniquely identify a record in a table?
A) Candidate key
B) Foreign key
C) Super key
D) Primary key
4. Which Python library formats data into dataframes?
A) NumPy
B) scikit-learn
C) NLTK
D) Pandas
5. What is a difference between Data Governance and Master Data Management (MDM)?
A) Data governance implementation involves people, policies, rules, and metrics.
MDM is done automatically with no interference.
B) Data Governance is a formalized system of decision making.
MDM is a comprehensive method to link all data elements to a common point of reference.
C) Data governance focuses mainly on IT practices.
MDM creates the rules and decisions for operational data processes.
D) Data governance is an informal system of decision making.
MDM is a formal system of decision making.
Solutions:
| Question # 1 Answer: A,B,D,E | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
