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Data Analyst

Total questions: 65

Worksheet time: 1hrs 5mins

Name
Class
Date
1.

What is the role of a data analyst?

a)

The role of a data analyst is to collect, process, and analyze data to provide insights and support decision-making.

b)

The role of a data analyst is to manage human resources

c)

The role of a data analyst is to create marketing campaigns

d)

The role of a data analyst is to design software programs

2.

What are the key skills required to be a successful data analyst?

a)

Proficiency in graphic design software

b)

Strong analytical skills, proficiency in programming languages such as SQL, Python, or R, data visualization skills, knowledge of statistical methods, and the ability to communicate findings effectively.

c)

Ability to speak multiple foreign languages

d)

Knowledge of ancient history

3.

Explain the process of data analysis.

a)

The process of data analysis involves only collecting and transforming the data.

b)

The process of data analysis involves collecting, cleaning, transforming, and applying statistical techniques to uncover patterns and insights within the data.

c)

Data analysis does not involve uncovering patterns and insights within the data.

d)

Data analysis is a simple process that does not require statistical techniques.

4.

What are the different types of data analysis?

a)

Qualitative, quantitative, and mixed methods analysis

b)

Linear, exponential, and logarithmic analysis

c)

Descriptive, diagnostic, predictive, and prescriptive analysis

d)

Static, dynamic, and interactive analysis

5.

What are the common tools used by data analysts?

a)

Microsoft Excel, SQL, Python, R, Tableau, Power BI

b)

C++

c)

HTML

d)

Java

6.

What is the importance of data visualization in data analysis?

a)

Data visualization is not important in data analysis because it is time-consuming and expensive.

b)

Data visualization is important in data analysis because it helps in understanding trends, patterns, and relationships in the data. It makes complex data more understandable and can lead to better insights and decision-making.

c)

Data visualization is not important in data analysis because it only adds unnecessary complexity to the data.

d)

Data visualization is not important in data analysis because it does not provide any meaningful insights.

7.

Explain the concept of data cleaning and its significance in data analysis.

a)

Data cleaning is the process of adding more errors to a dataset to improve its quality.

b)

Data cleaning is insignificant in data analysis because errors and inconsistencies do not affect the insights.

c)

Data cleaning is the process of identifying and correcting errors or inconsistencies in a dataset to improve its quality. It is significant in data analysis because clean data leads to more accurate and reliable insights, reduces the risk of making incorrect conclusions, and improves the overall quality of the analysis.

d)

Data cleaning is the process of analyzing clean data to identify errors and inconsistencies.

8.

What are the steps involved in the data analysis process?

a)

Defining the problem, analyzing the data, interpreting the results

b)

Collecting data, organizing the data, communicating the findings

c)

Defining the problem, collecting data, cleaning and organizing the data, exploring and analyzing the data, interpreting the results, and communicating the findings.

d)

Cleaning the data, exploring the data, defining the problem

9.

How does a data analyst use statistical methods in their work?

a)

To analyze and interpret data, identify trends and patterns, make predictions, and support decision-making.

b)

To create visualizations and reports

c)

To design experiments and conduct surveys

d)

To write code and develop software

10.

What are the ethical considerations in data analysis?

a)

Manipulating data to fit a desired outcome

b)

Selling the data to the highest bidder

c)

Ethical considerations in data analysis include ensuring data privacy, obtaining informed consent, avoiding bias in data collection and analysis, and being transparent about the methods and results.

d)

Ignoring data security measures

11.

Apa definisi dari machine learning?

a)

a) Proses pengolahan data tanpa penggunaan komputer

b)

b) Mesin yang dapat belajar sendiri tanpa arahan dari pengguna

c)

c) Program yang hanya dapat diprogram ulang oleh pengguna

d)

d) Teknologi yang tidak memerlukan data untuk berfungsi

12.

Mengapa semi-supervised learning digunakan dalam praktik?

a)

a) Ketika semua data memiliki label yang jelas dan tersedia

b)

b) Ketika hanya ada sedikit data berlabel dan banyak data tidak berlabel

c)

c) Ketika data tidak perlu dianalisis

d)

d) Ketika tidak ada data yang perlu dikumpulkan

13.

Apa yang dimaksud dengan "clustering" dalam unsupervised learning?

a)

a) Mengelompokkan data secara acak tanpa mempertimbangkan karakteristik

b)

b) Mengelompokkan data berdasarkan kesamaan untuk menemukan pola

c)

c) Memprediksi nilai berdasarkan variabel lain

d)

d) Menggunakan data berlabel untuk menentukan kategori

14.

Dalam evaluasi model machine learning, apa yang dimaksud dengan "overfitting"?

a)

a) Model tidak dapat mengenali data baru dan hanya bekerja pada data pelatihan

b)

b) Model bekerja dengan baik pada data pelatihan dan juga data pengujian

c)

c) Model gagal mempelajari pola dari data pelatihan

d)

d) Model hanya dapat digunakan dalam situasi tertentu

15.

Apa yang menjadi ciri utama dari supervised learning?

a)

a) Data yang digunakan tidak memiliki label

b)

b) Model dilatih menggunakan dataset yang memiliki label yang jelas

c)

c) Hanya memerlukan dataset kecil untuk pelatihan

d)

d) Dapat digunakan untuk menentukan pola dalam data tanpa intervensi manusia

16.

Dalam konteks supervised learning, teknik mana yang paling tepat digunakan untuk mengklasifikasikan data?

a)

a) K-Means Clustering

b)

b) Decision Tree

c)

c) Apriori Algorithm

d)

d) Principal Component Analysis (PCA)

17.

Dalam situasi apa Anda lebih suka menggunakan unsupervised learning dibandingkan supervised learning?

a)

a) Ketika semua data yang ada memiliki label yang jelas dan cukup

b)

b) Ketika Anda ingin menemukan kelompok alami dalam sebuah dataset tanpa pengetahuan awal tentang label

c)

c) Ketika Anda ingin memastikan hasil prediksi sangat akurat

d)

d) Ketika data tidak memiliki fitur yang cukup untuk analisis

18.

Apa yang menjadi keunggulan utama dari Convolutional Neural Networks (CNN) dibandingkan dengan jaringan saraf tradisional dalam pengolahan gambar?

a)

a) CNN tidak memerlukan preprocessing data yang ekstensif

b)

b) CNN bisa bekerja dengan data berlabel dan tidak berlabel secara bersamaan

c)

c) Arsitektur CNN memungkinkan ekstraksi fitur otomatis dari gambar melalui lapisan konvolusi dan pooling

d)

d) CNN hanya terdiri dari dua lapisan, sehingga lebih sederhana untuk dioperasikan

19.

Dalam konteks CNN, apa peran dari lapisan pooling?

a)

a) Untuk menambah kompleksitas model dengan lebih banyak neuron

b)

b) Untuk memperkecil ukuran data dan mengurangi jumlah parameter, sehingga mencegah overfitting

c)

c) Untuk mengganti fungsi aktivasi dengan yang lebih kompleks

d)

d) Untuk secara manual mengekstrak fitur dari gambar menggunakan filter

20.

Apa yang dimaksud dengan "feature maps" dalam deep learning, khususnya dalam arsitektur CNN?

a)

a) Representasi dari output akhir setelah seluruh data telah diproses

b)

b) Data yang sudah ditransformasikan menjadi format yang dapat digunakan oleh algoritma lain

c)

c) Hasil dari operasi konvolusi yang menunjukkan fitur yang terdeteksi oleh filter CNN di setiap lapisan

d)

d) Alat untuk mengevaluasi performa model dalam pengenalan pola

21.

A ___________ analyzes numeric data and uses it to help companies make better decisions.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

22.

A ___________ involves in preparing data. They develop, construct, test & maintain complete architecture.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

23.

A _____'s primary skill set revolves around data acquisition, handling, and processing.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

24.

Most entry-level professionals interested in getting into a data-related job start off as...

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

25.

A ______ needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization. 

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

26.

______ skills include advanced statistical analyses, a complete understanding of machine learning, data conditioning etc.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

27.

A _______________analyzes and interpret complex data. They are data wranglers who organize (big) data.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

28.

A _____ needs to be a master of both - data, stats, and math along with in-depth programming knowledge for Machine Learning and Deep Learning.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

29.

A _____ can earn up to $90,8390 /year.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

30.

To be successful in _____ requires solid programming skills, statistics knowledge, analytical skills, and an understanding of big data technologies.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

31.

This could be the tech stack of a...

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

32.

The typical salary of a _____ is just under $59,000 /year.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

33.

A _____ can earn 20 to 30% more than an average data engineer. Job postings from companies like Facebook, IBM and many more quote salaries of up to $136,000 per year.

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

34.

This could be the tech stack of a...

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

35.

This could be the tech stack of a...

a)

Data Analyst

b)

Data Engineer

c)

Data Scientist

36.

_________ is a measurable value that demonstrates how effectively a company is achieving a key business objective

a)

Key Performance Indicator

b)

Data Transformation

c)

Dashboard

d)

Metrics

37.

Who is the author of "A Business Intelligence System"

a)

Hans smith

b)

Hans peter

c)

John McCarthy

d)

Alen turing

38.

BI tools may have one or more of these functionalities

a)

Predictive and perspective analytics

b)

Cleaning the data

c)

Reporting

d)

Business performance management

39.

______ is a process of extracting, manipulating, visualizing, maintaining data as well as generating predictions.

a)

Data Science

b)

Data Encryption

c)

Data Transformation

d)

Data Visualization

40.

The prime purpose of Data analytics is?

a)

Convert and cleanse data

b)

Make predictions

c)

Support decision making

d)

Generate data

41.

Question to ask in BI is________

a)

Why did this not happen?

b)

What happened?

c)

How did this happen?

d)

What will not happen?

42.

Which of these are the different data mining techniques?

a)

Clustering

b)

Data removal

c)

Classification

d)

outer

43.

What is a data warehouse?

a)

Maximum storage of unknown data from one or more sources

b)

Minimum storage of structured data from one or more sources

c)

Average of unstructured data from one or more sources

d)

Aggregation of structured data from one or more sources

44.

The data with an exact meaning is ________

a)

Data

b)

Structured file

c)

Information

d)

Data mining

45.

Which of these are the two types of qualitative data?

a)

Nominal

b)

Ordinal

c)

Discrete

d)

Continuous

46.

Which of these are the two types of quantitative data?

a)

Nominal

b)

Ordinal

c)

Discrete

d)

Continuous

47.

Which of these are the skill required to be a data analyst

a)

Data Visualization

b)

Machine Learning

c)

Statistical Knowledge

d)

Data Cleaning

e)

Microsoft Excel

48.

Tools used by data analysts

a)

Microsoft Power BI

b)

Sisense

c)

Hadoop

d)

Spark

49.

_______ is said to be the secret sauce of Big data analytics

a)

Speed

b)

Automation

c)

Time

d)

Size of the data

50.

Famous BDA tools are

a)

Hadoop

b)

Spark

c)

MongoDB

d)

Tablue

51.
Which of the following statements is TRUE with respect to when the test analyst should become involved during different software development lifecycle models?
a)
In sequential V-model projects the test analyst should start test analysis concurrently with coding
b)

In sequential V-model projects the test analyst should start test analysis concurrently with requirement specification

c)
There are no differences in the moment of involvement for test analysts for the various software development lifecycles
d)
In Agile software development the test analyst should start test analysis and design concurrently with coding
52.
Which of the following answers describes the most appropriate and complete set of activities for the Test Analyst to focus on during test analysis and design?
a)
Analyze the test basis, select test techniques, create high-level test conditions for risk mitigation, create test cases to achieve desired coverage of the test basis, create risk mitigation test cases
b)
Analyze risks, create test conditions to address risks, create high-level test cases to meet test conditions for risk mitigation, create all low-level test cases
c)
Select test techniques, create high-level test cases to meet test conditions, create highlevel test cases to mitigate risks, create low level tests cases to achieve desired coverage
d)
Analyze the test basis, identify test conditions at appropriate levels to address the test basis, add test conditions for risk mitigation, select test techniques to achieve desired coverage, design test cases
53.
Which of the following statements does NOT give a good reason why test cases should be reviewed and understood by stakeholders?
a)
Customer and users review the test cases in order to verify them against requirements, business processes and business rules
b)
The test manager reviews the test cases in order to control the work of the test analyst and to create the organization’s test strategy
c)
Testers review test cases written by other testers in order to ensure that the test cases are consistent, understandable and executable by testers other than the author
d)
Developers review test cases written by testers in order to align their understanding of requirements with the testers’ and to align component testing with system testing
54.
According to the scenario depicted in the image, which of the following are the BEST options? (Select TWO options).
a)
In project HIPPOS the test cases should be written at a high level allowing the testers flexibility in varying the details to achieve higher coverage
b)
In project IQ the test cases should be written at a high level. The testers are business users and they know their business rules and calculations so no need for detailed documentation
c)
In both project IQ and HIPPOS the test cases must be written as low-level test cases, with thorough documentation and detailed procedures
d)
In project IQ the test cases should be written at a low level with documented procedures and traceability to requirements
e)
In project HIPPOS the test cases should be written at a low level with documented procedures and audit trails
55.
Based only on the given information (see the image), which of the following statements best describes the level of detail and documentation required for the test cases in this scenario?
a)
Low-level test cases should be written with detailed test procedures and documentation. Traceability from the low-level test cases to the requirements should be also assured
b)
Low-level test cases should be written because the testers are domain experts without a proper knowledge of formal testing
c)
High-level test cases should be written because requirements specification for B-OTC is very clear, detailed and exhaustive
d)
High-level test cases should be written without spending time on documentation. Traceability should be assured by using test case naming conventions
56.
Which of the following statements is INCORRECT regarding test implementation activities?
a)
Test Analysts may create data to be used with keyword-driven test automation
b)
If a risk-based test strategy is being used, risk priority order may dictate the execution order for the test cases
c)
When creating the test execution schedule, manual and automated test execution are considered to be independent activities
d)
Test Analysts must verify the procedures that gather data for evaluating current status against exit criteria
57.
According to the scenario depicted in the image, Which of the following possible risk mitigation actions should be prioritized highest?
a)
Review the calculation algorithms and work with specialists to define a data set for calculation tests
b)
Defer usability testing until UAT and recruit visually impaired testers to join the UAT team
c)
Involve visually impaired users in the review of the user interface design
d)
Spend time with developers to identify operational scenarios to test performance
58.
On the last project, insufficient resources were allocated for the configuration testing. When should this area be addressed for the next project?
a)
During planning
b)
During implementation
c)
During execution
d)
During closure
59.

Which of the following is an activity the Test Analyst should perform to support monitoring and controlling the test project?

a)
Plan the testing to align with the software lifecycle
b)
Ensure accurate and timely information is recorded for root causes of defects
c)
Provide input to the test manager regarding test estimates
d)
Test the installation procedures
60.
According to the scenario depicted in the image, which type of test cases should be written?
a)
Concrete test cases for both regression testing and UAT
b)
Logical test cases for both regression testing and UAT
c)
Concrete test cases for regression testing and logical test cases for UAT
d)
Logical test cases for regression testing and concrete test cases for UAT
61.

Enter Your Name

4 lines
62.

Enter Your Enrolment No

4 lines
63.

Enter Your Branch(CSE, CSE-DS, CSE-AIML, CSIT, CY, IT)

4 lines
64.

Enter Your Acropolis Email ID

4 lines
65.

Enter Your Batch(2026 or 2027)

4 lines

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