Structured data refers to highly organized data stored in predefined formats such as rows and columns.

Relational databases (Option A): Structured.

Data tables (Option B): Structured.

CSV files (Option C): Structured because they are tabular with rows and columns.

XML (Option D): Not structured but semi-structured because it stores hierarchical information with tags and attributes.

Thus, the correct answer is Option D (XML).

Functional programming (FP) is a paradigm widely adopted in data science and big data tools (e.g., Spark with Scala/Python). Its useful features include:

Option A (Higher-Order Functions): Functions can take other functions as arguments or return them, enabling powerful abstractions like map(), reduce(), and filter().

Option B (Immutable Data): Ensures reliability and thread-safety, crucial for distributed computing. Once created, data structures cannot be modified, preventing side effects.

Option C (Lazy Evaluation): Computations are delayed until results are needed, improving performance in large-scale data operations.

Since FP leverages all three features, the correct answer is Option D (All of the above).

The Hadoop ecosystem consists of multiple components, but the two core components that define Hadoop are:

HDFS (Hadoop Distributed File System): Provides fault-tolerant, scalable storage across distributed clusters. It is the backbone for storing massive datasets in a distributed fashion.

MapReduce Framework: Provides the parallel computing and data processing layer in Hadoop, enabling batch analysis over distributed datasets.

Option A: Correct, HDFS is essential.

Option B: Correct, MapReduce is essential.

Option C: Incorrect, Spark is a newer processing framework, but it is not originally part of Hadoop core.

Option D: Correct answer since both HDFS and MapReduce are considered the fundamental parts of Hadoop.

Option E: Incorrect, because Spark is not a core Hadoop component (though it integrates with Hadoop).

Apache Spark is an open-source distributed computing framework widely used for big data processing and machine learning pipelines.

The core implementation of Spark is written in Scala (Option A), which runs on the JVM (Java Virtual Machine).

Spark also provides APIs for Java, Python (PySpark), R, and SQL, but its native language is Scala.

Options C (C) and D (C++) are incorrect; Spark is not written in these languages.

Python (Option E) is a supported API, but Spark itself is not written in Python.

Thus, the correct answer is Scala (Option A).

Customer Lifetime Value (CLTV) is a predictive metric estimating the total revenue a business can reasonably expect from a customer during their entire relationship.

Statement i: Correct. Many organizations calculate CLTV using historic transactional data, often looking at sales records over the past 12–18 months to establish baselines.

Statement ii: Correct. The primary purpose of CLTV is to help marketing, sales, and retail teams understand customer value, enabling them to allocate budgets effectively for retention, promotions, and personalized marketing.

Thus, both statements are correct → Option C (Both i and ii).