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EMC D-DS-OP-23 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation and Feature Engineering | 20% | - Feature Engineering Techniques
|
| Machine Learning Model Optimization | 25% | - Model Tuning and Evaluation
|
| Deployment and Operationalization | 15% | - Model Deployment
|
| Data Engineering Pipeline Optimization | 25% | - Pipeline Design and Performance
|
| Data Governance and MLOps | 15% | - Governance and Operations
|
EMC Dell Data Scientist and Data Engineering Optimize 2023 Sample Questions:
1. Which IoT data processing tool enables businesses to make real-time decisions to capitalize on the Analytics of Things (AoT)?
A) Pravega
B) Apache NiFi
C) Apache Kafka
D) Nautilus
2. What is the primary focus of the EdgeX Foundry architecture?
A) Cloud computing
B) Big data analytics
C) Quantum computing
D) Edge computing and IoT devices
3. Which of the following data structures in Python is immutable?
A) Dictionary
B) Tuple
C) Set
D) List
4. In order for a table to be normalized in 4NF, what two conditions must be met?
A) Table is in BCNF
No multi-valued dependencies exist
B) Table is in 3NF
No dependencies exist
C) Table is in BCNF
Multi-valued depen
D) Table is in 3NF
No multi-valued dependencies exist
5. What is a data pipeline best practice related to Extract-Transform-Load (ETL) jobs?
A) ETL jobs should be manually executed and monitored to ensure the highest quality.
B) ETL jobs should be flexible to source data changes such as new variables or attributes.
C) A failed ETL job should be re-run immediately without taking any corrective actions.
D) A failed ETL job should always be marked as completed so that any other scheduled jobs can execute.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: B |
