Images are broadly categorized based on how they store visual information:

Rasterized images (Option i):

Composed of a grid of pixels (bitmap).

Each pixel has color information.

Examples: JPEG, PNG, BMP.

Best for photos or complex visuals.

Vectorized images (Option ii):

Composed of paths defined by mathematical formulas.

Scalable without quality loss.

Examples: SVG, EPS, AI.

Best for logos, icons, and illustrations.

Sectorized images (Option iii):

Not a standard category in computer graphics.

Thus, image files are categorized into Rasterized and Vectorized, making Option A (i, ii) correct.

Apache Spark is a distributed computing framework designed as an improvement over Hadoop’s MapReduce. According to the official Apache Spark documentation:

Spark can run workloads up to 100x faster in memory.

Spark can run workloads up to 10x faster on disk.

This performance gain comes from Spark’s use of in-memory computation, DAG execution engine, and optimized query execution, compared to the slower, disk-heavy Hadoop MapReduce framework.

Thus, the correct statement is Option A.

Use Case Development in data science projects involves identifying business needs and mapping analytics to business decisions. The standard steps include:

Option A: Understanding the key initiatives or challenges.

Option B: Identifying the key stakeholders.

Option C: Capturing the decisions stakeholders must make.

Option E: Brainstorming the questions stakeholders need answered to support decisions.

However:

Option D (Brainstorm the outcomes stakeholders need to answer): Incorrect phrasing. It is not “outcomes” that are brainstormed but questions and decisions.

Thus, the correct answer is Option D.

The Chief Data Monetization Officer (CDMO) is responsible for driving value extraction from data assets, not owning infrastructure. By contrast, the Chief Information Officer (CIO) traditionally owns technology infrastructure, including data lakes, warehouses, and data architectures.

Statement i: Correct. CDMO should NOT own data infrastructure. Their role is strategic — monetizing insights, not managing systems.

Statement ii: Incorrect. CDMO focuses on monetization, not architecture ownership.

Statement iii: Incorrect. CIO indeed does own these systems.

Statement iv: Correct. CIO owns the data lake, data warehouse, and underlying architectures/technologies.

Thus, the correct combination is i and iv → Option C.

Self-driving cars (autonomous vehicles) are an application of Reinforcement Learning (RL) in machine learning:

In RL, an agent (car) interacts with an environment (roads, obstacles, traffic) and learns to maximize rewards (e.g., safe driving, efficient navigation).

The system improves performance through trial-and-error learning, guided by reward signals such as staying in a lane or avoiding collisions.

Supervised learning (A): Used in some supporting tasks like image recognition (e.g., identifying stop signs), but not the core paradigm for self-driving.

Unsupervised learning (B): Useful for clustering sensor data, but again not the main paradigm.

Reinforcement learning (C): Correct, since self-driving fundamentally depends on RL decision-making.

Thus, the correct answer is Option C (Reinforcement Learning).

Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a model by maximizing the likelihood function — i.e., finding the parameters that make the observed data most probable.

Option A: Correct. MLE provides a framework for a large class of problems in data science, including regression, classification, generative models, and probabilistic inference.

Option B: Incorrect — it applies to many problems, not just a small subset.

Option C & D: Incorrect. HDFS (Hadoop Distributed File System) is a storage technology, unrelated to MLE.

Option E: Incorrect because C is invalid.

Thus, the correct answer is Option A (Large class of problems in Data Science).

A Big Data Strategy document provides a framework for aligning data initiatives with organizational objectives. It typically includes:

Business strategy (Option A): Ensures that big data initiatives align with overall corporate strategy.

Key decisions (Option B): Identifies the decisions data will help optimize or automate.

Key business initiatives (Option D): Links big data projects with critical organizational initiatives.

Key business entities (Option E): Defines the core entities (customers, products, channels) around which data will be organized.

However:

Business decisions (Option C): This is redundant and not a standard section; "key decisions" covers this aspect already.

Thus, the correct answer is Option C (Business decisions).

Inductive learning techniques in machine learning (such as decision trees, neural networks, or SVMs) follow a systematic sequence of steps for classification:

Training Step (i): A model is built using training data, where the system learns relationships between features and target labels.

Test Step (ii): The trained model is evaluated on unseen test data to measure its performance and generalizability.

Validation Step (iii): Often, a validation set is used to fine-tune model parameters, avoid overfitting, and choose the best model configuration.

Application Step (iv): The final validated model is applied to classify new, real-world data.

Since all four steps (i, ii, iii, iv) are essential to inductive classification, the correct answer is Option D (i, ii, iii, iv).

Apache SpamAssassin is one of the most widely used open-source tools for spam email detection.

It applies a rule-based system combined with Bayesian filtering, heuristics, and collaborative filtering methods to classify incoming emails as spam or legitimate.

Option A (Spam emails): Correct, this is the main function.

Option B (Big attachments): Incorrect. Large attachment filtering is not its primary purpose.

Option C (Email with virus): Incorrect. That falls under antivirus or malware detection tools, not SpamAssassin.

Option D: Incorrect since A is valid.

Thus, the correct answer is Option A (Spam emails).

Rehashing is a technique used in dynamic hash tables when the load factor (ratio of entries to bucket size) exceeds a certain threshold. It ensures efficient lookup, insertion, and deletion operations.

Option A (Correct): A larger hash table is allocated in memory to accommodate more entries.

Option B (Correct): A new hash function is typically required to map keys into the expanded table range.

Option C (Correct): All key-value pairs from the old table are re-inserted (rehashed) into the new table using the new hash function.

Since all three conditions (A, B, and C) are true, the best choice is Option E (All of the above).