Data & AI Infrastructure

Question 1

Define:

Big Data

Answer 1

It refers to extremely large data sets that are challenging to process and analyze using traditional data-processing tools due to their volume, velocity, and variety.

Examples include social media data, sensor data, and transaction records from e-commerce platforms.

Question 2

True or False:

Big Data can only be stored on physical servers.

Answer 2

False

Big Data can also be stored in the cloud, which offers scalable and flexible storage solutions.

Question 3

What is cloud computing?

Answer 3

The delivery of computing services, including servers, storage, databases, networking, and software, over the internet (‘the cloud’).

This allows for on-demand access to computing resources without the need for owning physical hardware.

Question 4

Fill in the blanks:

Cloud computing offers ______ and ______ resources for data storage and processing.

Answer 4

scalable; flexible

Question 5

What are GPUs and TPUs used for in AI?

Answer 5

GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) are specialized hardware designed to accelerate the training and inference of machine learning models by handling complex computations efficiently.

GPUs are often used in gaming and graphics, while TPUs are specifically built by Google for machine learning tasks.

Question 6

True or False:

TPUs are general-purpose processors like CPUs.

Answer 6

False

TPUs are designed specifically for accelerating machine learning computations, unlike general-purpose CPUs.

Question 7

What is a data pipeline in the context of AI?

Answer 7

It is a series of processes that automate the movement and transformation of data from various sources to a destination where it can be analyzed and used for AI models.

Data pipelines ensure data flows smoothly and is prepared for analysis.

Question 8

Fill in the blanks:

In a data pipeline, data is ______, ______, and ______.

Answer 8

collected; transformed; loaded

Question 9

What does model deployment mean in AI?

Answer 9

It refers to the process of integrating a machine learning model into an existing production environment to make predictions on new data.

This is the stage where models start to deliver real-world value.

Question 10

Which infrastructure component is crucial for handling large-scale AI computations efficiently?

Answer 10

GPUs/TPUs

They are optimized for parallel processing, making them ideal for handling the large computational demands of AI models.

Question 11

Explain the relationship between cloud computing and model deployment.

Answer 11

Cloud computing provides the infrastructure needed for deploying AI models at scale, offering resources like servers and storage that can be accessed remotely.

This enables models to be accessed and used globally without requiring physical hardware.

Question 12

True or False:

A data pipeline can include steps for data cleaning and preprocessing.

Answer 12

True

Data pipelines often involve cleaning, transforming, and organizing raw data to make it suitable for analysis or model training.

Question 13

Fill in the blank:

Big Data often requires advanced _________ for effective analysis and utilization.

Answer 13

infrastructure

Question 14

Provide an example of how cloud computing facilitates AI applications.

Answer 14

Cloud computing allows AI applications to scale quickly by providing on-demand resources, enabling services like virtual assistants to process and respond to vast amounts of data in real-time.

Examples include Amazon Alexa or Google Assistant.

Question 15

What is one advantage of deploying AI models in the cloud?

Answer 15

The ability to scale resources dynamically, allowing for efficient handling of variable workloads and user demands.

This ensures consistent performance across different levels of demand.