1.
This refers to immense volumes of data, both unstructured and structured, that can be
used to analyze insights which can lead to better decisions and strategic business
moves. Big Data
2. The following are included in the ten v’s of Big Data EXCEPT Visual
3. Which of the following common types of big data? All of the Above
4. Which of the following is/are ALWAYS TRUE about data science? both I and II
5. In obtaining data science process, the following questions are asked EXCEPT Are there
anomalies? Or How were the data sampled?
6. Which of the following are components of data analytics? All of the Above
7. The following is true about data analytics EXCEPT focus lies in describing the data in its
entirety.
SHORT QUIZ 1
1. Which of the following is always TRUE about Big Data? I only
I. Due to its size or structure, Big Data cannot be efficiently analyzed using
only traditional databases or methods.
II. II. Although the variety of Big Data tends to attract the most attention,
generally the volume and velocity of the data provide a more apt definition
of Big Data.
2. Among the business drivers that push businesses to become more analytical and data
driven, this one involves customer churn, fraud and default. Identify business risk
3. Which of the following are problems encountered in traditional data architecture? All of
the above
4. This are centralized data containers in a purpose-built space that supports business
intelligence and reporting but restricts robust analyses. Data warehouses
5. Which of the following TRUE about the differences of Business Intelligence (BI) and
Data Science? II only
I. Where Data Science problems tend to require highly structured data
organized in rows and columns for accurate reporting, BI projects tend to
use many types of data sources, including large or unconventional
datasets.
II. Data Science tends to be more exploratory in nature and may use
scenario optimization to deal with more open-ended questions.
6. This type of data has no inherent structure, which may include text documents, PDFs,
images, and video. Unstructured data
7. Which of the following is true about the current analytical architecture? Both I and II
I. Data sources are first loaded into the data warehouse where data needs
to be well understood, structured, and normalized with the appropriate
data type definitions. This kind of centralization enables security, backup,
and failover of highly critical data.
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II. Once in the data warehouse, data is read by additional applications
across the enterprise for BI and reporting purposes. These are high-
priority operational processes getting critical data feeds from the data
warehouses and repositories.
8. Which of these attributes stand out as defining Big Data characteristics? All of the above
SHORT QUIZ 2
1. Which of the following key roles in the new big data ecosystem has members who
possess a combination of skills to handle raw, unstructured data and to apply complex
analytical techniques at massive scales? Deep analytical talent
2. The data now is said to come from many sources including? All of the above
3. Which of the following group of players in the data value chain makes sense of the data
collected from various entities? Data Aggregators
4. The following are the skillsets and behavioral characteristics a data scientist must
possess EXCEPT? Qualitative skill
5. Which of the following describe the decade beyond 2010 in regards to big data? I only
I. In this era, everyone and everything is leaving a digital footprint.
II. Data volumes in this decade are measured in terms of petabytes.
6. Examples that fall under this group includes financial analysts, market research analysts,
life scientists, operations managers, and business and functional managers. Data savvy
professionals
7. The following are recurring sets of activities that data scientist performs EXCEPT?
Provide technical expertise to support analytical projects such as provisioning and
administrating analytical sandboxes.
SHORT QUIZ 3
1. Which of the following person provides the funding and gauges the degree of value from
the final outputs of the working team in a data analytics project? Project sponsor
2. In this phase of the data analytics life cycle, the team assesses the resources available
to support the project in terms of people, technology, time, and data. Discovery
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3. This refers to the process of cleaning data, normalizing datasets, and performing
transformations on the data. Data conditioning
4. Which of the following key questions are helpful to ask during the discovery phase when
interviewing the project sponsor? All of the above
5. The following is part of the data preparation phase EXCEPT? Developing initial
hypothesis
6. Which of the following describe the key role of Data Engineer? Executes the actual data
data extractions and performs substantial data manipulation to facilitate the
analytics.
7. Which of the following activity is NOT involve in identifying potential data sources?
Perform extract, transform, load processes to
data
8. The following activities is part of the discovery phase EXCEPT? The team catalog the
data sources that the team has access to and identify additional data sources that the
team can leverage.
9. Which of the following is TRUE about data analytics life cycle? Both I and II
I. A common mistake made in data science projects is rushing into data
collection and analysis, which precludes spending sufficient time to plan
and scope the amount of work involved, understanding requirements, or
even framing the business problem properly.
II. Having a good data analytics process ensures a comprehensive and
repeatable method for conducting analysis and helps focus time and
energy.
10. In this phase of the data analytics life cycle, the team delivers final reports, briefings,
code, and technical documents. Operationalize
SHORT QUIZ 4 (10/16)
1. Which of the following is a deliverable under the operationalize phase? All of the above
2. Which of the following is TRUE about model planning? Both I and II
I. Under this phase, the team develop datasets for training, testing, and production
purposes.
II. Data Exploration, Variable and Model selection characterize this phase.
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3. The following activities are involved under the model planning phase EXCEPT? Assess
the validity of the model and its results.
4. Which of the following are activities done under phase 5 of data analytics life cycle? All
of the above
5. Which of the following are free or open source tools available for data analytics
practitioner? Octave
6. Which of the following is TRUE about the final phase of data analytics life cycle? I only
I. In the final phase, the team communicates the benefits of the project more
broadly and sets up a pilot project to deploy the work in a controlled way before
broadening the work to a full enterprise or ecosystem of users.
II. Under this phase, the team reflect on the project and consider what obstacles
were in the project and what can be improved in the future as well as make
recommendations for future work or improvements to existing processes.
7. Which of the following is TRUE about model building? Both I and II
I. The phases of model planning and model building can overlap quite a bit, and in
practice one can iterate back and forth between the two phases for a while before
settling on a final model.
II. Although the modeling techniques and logic required to develop models can be
highly complex, the actual duration of this phase can be short compared to the time
spent preparing the data and defining the approaches.
8. In creating robust models, the following questions needs to be considered EXCEPT?
How consistent are the content and files?
SHORT QUIZ 5 (14/16)
1. Which of the following statements is/are ALWAYS TRUE? Both I and II
I. Inferential statistics consists of Estimation and Hypothesis Testing
II. The link between inferential and descriptive statistics is probability
2. The following characterizes inferential statistics EXCEPT? Present data
3. In predicting Sales Revenue using TV and Radio Ads Expenses, we have the following
regression results. Estimate the predicted sales if tv and radio ads expenses are 200
and 50 respectively. 21.5
4. Which of the following is/are ALWAYS TRUE about regression analysis? I only
I. It’s the technique used most frequently to analyze the relationship
between two or more variables.
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II. Predictor variables could either be discrete or continuous.
5. Which of the following is/are ALWAYS TRUE about simple regression? II only
I. Simple regression attempt to predict the dependent variable using more
than one independent variable.
II. Simple regression consists of one regression coefficient for each
explanatory variable.
6. Prior to any regression modelling, the data should always be inspected for the following
EXCEPT? Expected pattern
7. In predicting Sales Revenue using Newspaper Ads Expenses, we have the following
regression results
Estimate the predicted sales if newspaper ads expenses is 60 units. 15.6
SHORT QUIZ 6 (12/12)
1. Based on the following results of logistic regression, what is the likelihood of churning when Age
= 40 and Churned_contacts = 5? (Note: Round coefficients up to 2 decimal places). 0.269
2. The following are examples of applications for logistic regression EXCEPT?
A model to determine the relationship of amount of income given age, education, number years working
and gender.
3. Based on the following results of logistic regression, which of the following statements is/are
TRUE? II only
I. For every 1 unit increased in Age, the value of logistic function increases by 0.16.
II. The regression coefficient for the Married variable is not significant.
4. Which of the following is TRUE about the logistic function? Both I and II
I. As the value of y increases, the likelihood of the event f(y) also increases.
II. The values of y are not directly observed but rather, only the value of f(y) in terms of success
or failure is observed.
5. Which of the following is TRUE about logistic regression? I only
I. When the outcome variable is categorical in nature, logistic regression can be used to predict the
likelihood of an outcome based on the input variables.
II. Logistic regression can only be applied to an outcome variable with two values such as true/false,
pass/fail, or yes/no.
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