Data
What is data? Give 1 manufacturing example.
Information collected from machines or processes.
What does AI stand for?
Artificial Intelligence, Machines or computer systems that can perform tasks that normally require human intelligence.
Jabil provides engineering, manufacturing, and supply chain solutions to customers across multiple industries. True or False?
True
Explain the difference between the Structured Data and Unstructured Data. Give two examples for each categories.
Structured Data: Data that is organized in a fixed format, usually in rows and columns (like a table), making it easy to store and analyze.
Unstructured Data: Data that does not have a fixed structure or format, making it harder to process without AI or advanced tools.
List any two examples of AI applications used in daily life. How its works ?
Voice assistants (Siri, Alexa)
Recommendation systems (Netflix, YouTube)
Chatbots
Facial recognition
Spam email detection
List any two industries that Jabil serves.
Healthcare
Automotive / Electric Vehicles
Cloud / Data Center Infrastructure
Consumer Electronics
Renewable Energy
Industrial
Explain the difference between training, validation and test data using a factory dataset.
Training data : to train the model
Validation data : to tune and improve it
Test data : to evaluate its performance on unseen data.
Name 3 types of Machine Learning used in manufacturing. How Its Learn ?
Supervised Learning: Uses labelled data
Unsupervised Learning: Uses unlabeled data finds hidden patterns or groups
Reinforcement Learning: Learns by trial and error with rewards/penalties
List two core services that Jabil offers to its customers.
Engineering and design services
Manufacturing solutions
Supply chain management
Product lifecycle management
Logistics and fulfillment
Why is data cleaning a critical step before data analysis or AI modeling? Give 4 reasons with examples.
Remove errors: Fix incorrect values (e.g., wrong temperature readings)
Handle missing data: Fill or remove missing values
Reduce noise: Remove random or abnormal fluctuations
Improve accuracy: Clean data gives better analysis and prediction
Make data consistent: Ensure same format (e.g., all in °C)
Explain the difference between Machine Learning and Deep Learning. Compare them with the Aspect of Feature extraction, Data Requirement, Complexity, Training Time.
Machine Learning is a subset of AI where computers learn from data and improve without being explicitly programmed.
Deep Learning is a subset of Machine Learning that uses neural networks with many layers (like a human brain).
Why do you think companies choose Jabil instead of doing everything themselves?
Saves time and cost
Access to expert manufacturing knowledge
Global presence and scale
Allows customers to focus on their core business
Flexibility and speed to market
In a manufacturing company, what is the difference between a Data Warehouse and a Data Lake, and how are both used to support factory operations and reporting?
A Data Warehouse stores structured and cleaned data such as production KPIs, yield rates, and defect reports, which are used for dashboards and decision-making.
A Data Lake stores raw factory data, such as machine sensor readings, IoT data, logs, and inspection images, which may not be structured.
Both are used together because the Data Lake collects all raw manufacturing data, and then important data is processed and moved into the Data Warehouse for reporting and analysis.
What is the difference between precision and recall, and which is more important in defect detection (why)?
Precision: all items predicted as defects, how many are actually defects? (Correctness)
Recall: all actual defects, how many did we correctly detect? (Completeness)
Recall is more important
Missing a real defect (false negative) can cause serious issues:
Product failures
Customer dissatisfaction
Safety risks (in manufacturing, medical, etc.)
Why do you think companies like Jabil are important to the global technology ecosystem?
They enable other brands to bring products to market
They support innovation without companies needing their own factories
They connect design, manufacturing, and supply chains
They help scale products globally