Last Updated: Aug 02, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Model Optimization | 25% | - Model Tuning and Evaluation
|
| Data Engineering Pipeline Optimization | 25% | - Pipeline Design and Performance
|
| Data Governance and MLOps | 15% | - Governance and Operations
|
| Data Preparation and Feature Engineering | 20% | - Feature Engineering Techniques
|
| Deployment and Operationalization | 15% | - Model Deployment
|
1. What is a common use case for a document-based NoSQL database like MongoDB?
A) Managing hierarchical data
B) Performing complex SQL queries
C) Storing structured tabular data
D) Tracking real-time data streams
2. What is the main programming language supported by Apache Spark?
A) Python
B) Java
C) C++
D) Ruby
3. What is a challenge of managing data pipelines?
A) Ensuring that proper backups are available in the event of a data corruption
B) Making the data volume consistent, or at least easily predictable
C) Monitoring is simply accomplished by the end users reporting any data quality issues
D) Guaranteeing there is no personally identifiable information in the extracted data
4. What is a common approach to handling errors and failures in data pipelines?
A) Implementing robust error handling and retries
B) Relying solely on input data validation
C) Allowing errors to propagate to end-users
D) Ignoring errors and continuing execution
5. What is a characteristic of Pig?
A) Uses HiveQL to translate SQL queries into MapReduce jobs
B) Performs real-time reads and writes in HDFS
C) Data warehouse infrastructure that manages jobs in the cluster
D) Alternative language to Java programming for MapReduce
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: D |
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