EN6106 – Data Science with Python

Important Instructions

This paper has 25 questions covering all sub-topics of Lesson 2: Data Science with Python.
Each question has 5 choices with ONE OR MORE correct answers.
All questions carry equal marks (4 marks each, total 100 marks).
There is a penalty for incorrect responses to discourage guessing.
To PASS, you must score at least 40 marks out of 100.
Click Submit Paper only when you have answered all questions.

Marking Scheme (per question — max 4 marks)

Score = [ (Correct choices selected ÷ Total correct choices) − (Wrong choices selected ÷ Total wrong choices) ] × 4
Minimum score per question = 0 | Maximum = 4.00

2.1 — Introduction to Data Science & Data Engineering

Q 01 Which of the following statements correctly describes what data is and how it can be categorised? 4 marks

(a)Data is a collection of information.

(b)Unstructured data is not organised and must be organised for analysis purposes.

(c)Structured data requires significant preprocessing before it can be used for any analysis.

(d)Structured data is organised and easier to work with compared to unstructured data.

(e)Data can only be stored in relational databases for it to be considered structured.

Question score:—

Q 02 Which of the following statements accurately describe Data Science? 4 marks

(a)Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data.

(b)The goal of Data Science is to gain insights and make data-driven decisions to solve complex problems.

(c)Data Science is focused exclusively on working with structured, tabular datasets.

(d)Data Science enables companies to make better decisions, perform predictive analysis, and discover patterns.

(e)Data Science involves techniques from statistics, machine learning, computer science, and domain knowledge.

Question score:—

Q 03 In which of the following fields is Data Science used? 4 marks

(a)Healthcare — to analyse patient data and improve diagnosis and treatment.

(b)Finance — to detect fraud, predict stock prices, and manage financial risks.

(c)Astrology — to generate personalised horoscope predictions for individuals.

(d)Sports — to analyse player performance and game data to gain insights.

(e)Transportation — to optimise routes, predict maintenance needs, and improve efficiency.

Question score:—

Q 04 Which of the following areas require expertise for a Data Scientist? 4 marks

(a)Machine Learning

(b)Statistics and Mathematics

(c)Civil and Structural Engineering

(d)Programming (Python or R)

(e)Databases

Question score:—

Q 05 Which of the following are steps typically followed by a Data Scientist when working on a project? 4 marks

(a)Defining the problem and understanding objectives.

(b)Collecting and cleaning the data from various sources.

(c)Configuring network firewalls and VPN tunnels for secure data transfer.

(d)Evaluating and deploying trained models to production.

(e)Communicating results and monitoring the deployed models.

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Q 06 Which of the following correctly describe Data Engineering or the role of a Data Engineer? 4 marks

(a)Data Engineering is the process of designing, building, maintaining, and troubleshooting the infrastructure and systems that support data collection, storage, and processing.

(b)Data Engineers work with big data technologies such as Apache Hadoop, Apache Spark, and Apache Storm.

(c)Data Engineers primarily focus on building and training machine learning models for deployment.

(d)Data Engineers ensure that data is accurate, reliable, and available for analysis and decision-making.

(e)Data Engineers design and build data pipelines that move data from various sources into a central repository or data lake.

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2.2 — Data Engineering Pipeline & Infrastructure

Q 07 Which of the following are correct statements about a data pipeline? 4 marks

(a)A data pipeline is a series of steps used to Extract, Transform, and Load (ETL) data from various sources to a central location.

(b)A data pipeline is designed to move data from one place to another in an automated and efficient manner.

(c)A data pipeline only works with data from a single, fixed data source.

(d)Sources that feed into data pipelines include databases, SaaS apps, data streams, and data lakes.

(e)Data pipelines improve security by restricting access to authorised teams only.

Question score:—

Q 08 Which of the following are steps in a typical data pipeline? 4 marks

(a)Extraction — data is extracted from various sources such as databases, APIs, and log files.

(b)Transformation — the extracted data is cleaned and validated for consistency and accuracy.

(c)Encryption — all data is encrypted at rest using AES-256 before any further processing.

(d)Loading — the transformed data is loaded into a central storage location such as a data lake or data warehouse.

(e)Analysis — data scientists and analysts use the processed data to identify patterns and gain insights.

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Q 09 Which of the following are components of data pipeline architecture? 4 marks

(a)Sources — where the information originates (APIs, cloud, relational databases, NoSQL, Hadoop).

(b)Standardisation — ensuring all data follows the same measurement units, formats, and presentation.

(c)Speculation — making educated guesses about data values when source data is missing.

(d)Correction — removing errors, corrupt records, and resolving confusing or invalid data.

(e)Automation — handling error detection, status reports, and monitoring continuously or on a schedule.

Question score:—

Q 10 All data pipelining tools share which of the following common requirements? 4 marks

(a)Extract data from multiple relevant data sources.

(b)Clean, alter, and enrich the data so it can be ready for analysis.

(c)Generate machine learning model predictions in real time before data is stored.

(d)Load the data to a single source of information, usually a data lake or a data warehouse.

(e)Provide Byzantine fault tolerance (BFT) for distributed data storage systems.

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Q 11 Which of the following are recognised types of data pipeline tools with an example correctly matched? 4 marks

(a)Batch — Informatica PowerCenter is a popular batch processing pipeline tool.

(b)Open-source — Apache Kafka and Apache Airflow are open-source pipeline tools.

(c)Blockchain — Ethereum is used as a real-time data pipeline tool for financial data.

(d)Cloud-native — Blendo and Confluent are tools optimised for cloud-based data.

(e)Real-time — StreamSets and Hevo Data are real-time pipeline tools designed for streaming sources.

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2.3 — Data-Driven Insights in Different Fields

Q 12 Which of the following correctly describe how data-driven insights are applied in specific fields? 4 marks

(a)Marketing — companies use data to identify trends and target specific audience segments more effectively.

(b)Manufacturing — analysing production and equipment data to identify improvement areas and reduce costs.

(c)Archaeology — reconstructing ancient civilisations entirely from satellite imagery without field work.

(d)Retail — analysing customer behaviour and sales to adjust inventory and pricing strategies.

(e)Public Sector — government agencies use data to inform policy decisions and improve service delivery.

Question score:—

Q 13 Which of the following are correct applications of data science in finance? 4 marks

(a)Detecting fraud in financial transactions.

(b)Predicting stock prices using historical financial data.

(c)Designing the visual layout and logo of bank branch offices.

(d)Managing financial risks through trend analysis.

(e)Making better investment decisions by analysing investment and spending patterns.

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2.4 — Using Data Science to Extract Meaning from Data

Q 14 Identify the correct steps used in data science to extract meaning from data. 4 marks

(a)Data Collection

(b)Data Cleaning

(c)Data Serialisation

(d)Data Modeling

(e)Data Interpretation

Question score:—

Q 15 Regarding the Data Cleaning step in data science, which of the following statements are true? 4 marks

(a)Data cleaning removes errors, outliers, and missing values from the collected data.

(b)Data cleaning ensures that the data is accurate and ready for analysis.

(c)Data cleaning is performed after data modelling to validate the model's output.

(d)The data science process often requires iteration between steps, especially when insights are unclear.

(e)After cleaning, data exploration uses visualisations, summary statistics, and correlation analyses.

Question score:—

Q 16 The Data Modeling step in the data science process involves which of the following? 4 marks

(a)Building statistical models to extract meaning from explored data.

(b)Applying machine learning techniques to make predictions or understand variable relationships.

(c)Publishing the raw uncleaned dataset to stakeholders for their own interpretation.

(d)Evaluating model performance using techniques such as cross-validation.

(e)Communicating findings to stakeholders such as business leaders and software engineers.

Question score:—

2.5 — Data Visualisation

Q 17 Which of the following are correct statements about data visualisation? 4 marks

(a)Data visualisation is the process of creating graphical representations of data to make it more easily understandable.

(b)Data visualisation tools provide an accessible way to see trends, outliers, and patterns in data.

(c)Data visualisation is only applicable in the field of business and finance.

(d)Data visualisation enables employees to present data to non-technical audiences without confusion.

(e)In the world of Big Data, data visualisation tools and technologies are essential for making data-driven decisions.

Question score:—

Q 18 Match the correct type of data visualisation to its description. Which pairings are correct? 4 marks

(a)Bar chart — used to compare the values of different categories.

(b)Line chart — used to show changes in data over time.

(c)Scatter plot — used to show the proportion of different categories as a whole.

(d)Heat map — used to show the distribution of data across a two-dimensional surface.

(e)Word Cloud — used to show the frequency of words in a text.

Question score:—

Q 19 Which of the following are recognised advantages of data visualisation? 4 marks

(a)Easily sharing information among different teams and stakeholders.

(b)Visualise patterns and relationships within data.

(c)Correlation shown in a visualisation always proves causation between two variables.

(d)Quickly identify trends and outliers when viewing a chart.

(e)Interactively explore opportunities within data.

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Q 20 Which of the following are recognised disadvantages of data visualisation? 4 marks

(a)Biased or inaccurate information may be presented through visualisations.

(b)Correlation doesn't always mean causation, which can lead to wrong conclusions.

(c)Data visualisation cannot be used with large datasets (Big Data).

(d)Core messages can get lost in translation when using complex visualisations.

(e)With many data points, it is easy to make inaccurate assumptions from a chart.

Question score:—

2.6 — Data Science & Python

Q 21 Which of the following are Python libraries used in data science and data analysis? 4 marks

Q 22 Which of the following are reasons why Python is popular for data science? 4 marks

(a)Python has a large and active community with many libraries and frameworks available for data science tasks.

(b)Python is easy to learn and use, even for people with little or no programming experience.

(c)Python is the only programming language that supports machine learning model development.

(d)Python is a versatile language that can be used for a wide range of tasks, from web development to machine learning.

(e)Python's libraries and frameworks are well-documented and supported, making it easy to find help and resources.

Question score:—

Q 23 Which of the following correctly describe the NumPy and pandas libraries in Python? 4 marks

(a)NumPy is a library for handling numerical data and arrays.

(b)pandas is a library for handling and manipulating data in a tabular format.

(c)NumPy is primarily used for building interactive web user interfaces in Python applications.

(d)Matplotlib and Seaborn are Python libraries used for creating visualisations of data.

(e)scikit-learn is a Python library used for machine learning tasks.

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Q 24 What are the two main types of data used in data analytics? 4 marks

Q 25 Which of the following are correct statements regarding batch processing data pipeline tools? 4 marks

(a)Batch processing tools are best suited for moving large amounts of data at regularly scheduled intervals.

(b)Batch processing tools are the best option when data is required in real time from streaming sources.

(c)Informatica PowerCenter is an example of a popular batch processing pipeline tool.

(d)IBM InfoSphere DataStage is a popular batch processing pipeline tool.

(e)Batch tools are suitable when you do not require data to be processed in real time.

Question score:—

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EN6106 — Emerging Topics in ITTopic 2: Data Science with Python

Important Instructions

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EN6106 — Emerging Topics in IT
Topic 2: Data Science with Python