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Google Data Analytics study materials
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Google Data Analytics practice questions

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Question 1Foundations: Data, Data, Everywhere

Which statement best describes data analytics?

  1. The process of storing business records in a secure database
  2. The set of legal rules that govern how data is stored
  3. The science of collecting, organizing, and analyzing data to support decisions Correct answer.
  4. The practice of writing code that automates financial reports

Explanation

Data analytics is the science of data: collecting, organizing, and analyzing it to draw conclusions, make predictions, and support informed decisions. The other options describe database administration, programming, and governance rather than analytics itself.

Question 2Foundations: Data, Data, Everywhere

An analyst converts thousands of raw sales records into a monthly summary that tells managers how the business is performing. The summary is best described as:

  1. Raw data
  2. A data model
  3. A data pipeline
  4. Information Correct answer.

Explanation

Data becomes information once it has been collected, processed, and given context so that it can be interpreted and used. Raw records alone lack that context, and models and pipelines are tools for handling data rather than meaningful outputs.

Question 3Foundations: Data, Data, Everywhere

Which phase of the data analysis process focuses on understanding the business problem and what stakeholders need?

  1. Prepare
  2. Share
  3. Process
  4. Ask Correct answer.

Explanation

In the Ask phase the analyst defines the problem, clarifies expectations with stakeholders, and frames the questions the analysis must answer. Prepare, Process, and Share are later phases dealing with data collection, cleaning, and communication.

Question 4Foundations: Data, Data, Everywhere

Which of these is an example of unstructured data?

  1. A table listing employee IDs and hire dates
  2. A product catalog with prices indexed by SKU
  3. A folder of recorded customer service calls Correct answer.
  4. A spreadsheet column of monthly sales totals

Explanation

Unstructured data has no predefined format or organization, which is true of audio recordings, videos, and free-form text. Spreadsheets, tables, and indexed catalogs store values in defined rows, columns, or fields, which makes them structured.

Question 5Foundations: Data, Data, Everywhere

Which of the following is an example of qualitative data?

  1. The number of minutes an order took to deliver
  2. The price of a product in dollars
  3. A customer's written comment about a product Correct answer.
  4. The number of items sold yesterday

Explanation

Qualitative data describes qualities and is gathered through observation, interviews, or open-ended responses, so a written comment qualifies. Counts, prices, and durations are numbers that can be measured or counted, making them quantitative.

Question 6Foundations: Data, Data, Everywhere

Which type of data describes categories that have a meaningful order or ranking?

  1. Ordinal Correct answer.
  2. Continuous
  3. Discrete
  4. Nominal

Explanation

Ordinal data labels categories that can be ranked, such as satisfaction levels from dissatisfied to very satisfied. Nominal data also uses labels but has no order, while discrete and continuous describe how numeric values are measured.

Question 7Foundations: Data, Data, Everywhere

What is a data ecosystem?

  1. A single centralized database that stores all of an organization's records
  2. A software tool that cleans and formats data before analysis
  3. The people, tools, and processes that produce and share data Correct answer.
  4. The group of analysts who report to one manager

Explanation

A data ecosystem is the network of elements, including people, tools, and processes, that interact to produce, manage, store, organize, analyze, and share data. It is broader than one database, one tool, or one team.

Question 8Foundations: Data, Data, Everywhere

An analyst surveys 500 of a company's 40,000 customers. What is the term for the 500 customers?

  1. The variable
  2. The sample Correct answer.
  3. The metadata
  4. The population

Explanation

A sample is a subset of a larger group collected for analysis, while the population is the entire group the analyst wants to draw conclusions about, here all 40,000 customers. Metadata describes data, and a variable is a characteristic being measured.

Question 9Foundations: Data, Data, Everywhere

A marketing team uses A/B test results to choose which email subject line to send to all customers. This is an example of:

  1. Root cause analysis
  2. Gap analysis
  3. Data-driven decision-making Correct answer.
  4. Data-inspired decision-making

Explanation

Data-driven decision-making uses facts and verified data from an experiment to guide a choice, which is exactly what the A/B test provides. Data-inspired decisions draw loosely on data to generate ideas, and gap and root cause analysis are diagnostic techniques rather than decision models.

Question 10Foundations: Data, Data, Everywhere

Sales at a store fell sharply last month. A manager keeps asking "why" after each explanation until the underlying cause is found. Which technique is being used?

  1. A/B testing
  2. Root cause analysis Correct answer.
  3. Gap analysis
  4. Predictive modeling

Explanation

Root cause analysis probes repeatedly beneath the surface of a problem, often using the five whys, to find the underlying reason rather than a symptom. Gap analysis compares current and desired states, and predictive modeling forecasts future outcomes.

Question 11Foundations: Data, Data, Everywhere

A company's customer satisfaction score is 72 percent and leadership wants it at 90 percent. Which approach examines what stands between these two figures?

  1. Regression modeling
  2. Data visualization
  3. Root cause analysis
  4. Gap analysis Correct answer.

Explanation

Gap analysis compares where the business is now with where it wants to be and identifies what is missing in between. Root cause analysis instead traces the source of a single problem, while modeling and visualization are supporting techniques.

Question 12Foundations: Data, Data, Everywhere

An insurance company builds a model that estimates how many claims each policyholder will file next year. Which problem type is this?

  1. Making predictions Correct answer.
  2. Categorizing things
  3. Spotting anomalies
  4. Identifying themes

Explanation

Making predictions uses existing data to forecast future outcomes, such as the number of claims a policyholder will file. Categorizing assigns items to groups, spotting anomalies finds unusual events, and identifying themes groups qualitative feedback.

Question 13Foundations: Data, Data, Everywhere

Which sequence lists phases of the data life cycle?

  1. Plan, Analyze, Report, Delete
  2. Ask, Prepare, Process, Analyze, Share, Act
  3. Plan, Capture, Manage, Analyze, Archive, Destroy Correct answer.
  4. Collect, Clean, Visualize, Present

Explanation

The data life cycle runs from planning and capturing data through managing, analyzing, and archiving it, and finally destroying it when it is no longer needed. The Ask-to-Act sequence describes the data analysis process rather than the life cycle.

Question 14Foundations: Data, Data, Everywhere

In data ethics, what does fairness require of an analyst?

  1. Reporting every statistical result, including errors
  2. Making sure the analysis does not create or reinforce bias against any group Correct answer.
  3. Sharing all collected data openly with the public
  4. Removing all personal identifiers before any analysis

Explanation

Fairness means ensuring that data collection and analysis do not produce unfair outcomes or amplify bias for particular groups. Open sharing is a practice associated with open data, and removing identifiers is a privacy safeguard rather than a definition of fairness.

Question 15Foundations: Data, Data, Everywhere

A city government publishes anonymized transit ridership data for anyone to use and republish. This is an example of:

  1. Data stewardship
  2. Data ownership
  3. Open data Correct answer.
  4. Data privacy

Explanation

Open data is data that is freely available for everyone to access, use, and share, which matches publishing ridership figures publicly. Data privacy restricts access to protect individuals, while stewardship and ownership describe responsibility for data rather than public availability.

Question 16Foundations: Data, Data, Everywhere

Which role is primarily responsible for building and maintaining the systems and pipelines that move data into an analytics environment?

  1. Data scientist
  2. Data analyst
  3. Data engineer Correct answer.
  4. Business analyst

Explanation

Data engineers design, build, and maintain the infrastructure and pipelines that transform raw data into usable formats. Data analysts interpret data for stakeholders, business analysts focus on process and business needs, and data scientists apply statistical and machine learning methods.

Question 17Foundations: Data, Data, Everywhere

What is metadata?

  1. Data that has never been processed or organized
  2. Data that has been deleted for privacy reasons
  3. Data that describes other data Correct answer.
  4. Data collected from social media sources

Explanation

Metadata is data about data; it describes how, when, and where other data was created and how it is structured, such as a file's creation date or a column definition. It is not the raw content itself and is unrelated to deletion or a particular source.

Question 18Foundations: Data, Data, Everywhere

Which of these is an example of continuous data?

  1. The number of customer complaints received
  2. The amount of time a visitor spends on a web page Correct answer.
  3. The number of employees in each department
  4. The count of products sold per store

Explanation

Continuous data can be measured on a scale and takes any value within a range, so a duration such as time on a page is continuous. Counts of employees, products, or complaints can only take whole-number values, which makes them discrete.

Question 19Foundations: Data, Data, Everywhere

An analyst studying employee satisfaction emails a survey only to staff who have worked at the company for more than five years. Which problem is most likely?

  1. The data set contains too many metadata fields
  2. The survey will produce nominal rather than ordinal data
  3. The sample is biased toward long-tenured employees Correct answer.
  4. The data is unstructured and cannot be analyzed

Explanation

Limiting the survey to long-tenured staff means the sample does not represent newer employees, so results will be skewed toward one group. The format of the data, its metadata, and its measurement level are not the central issue here.

Question 20Foundations: Data, Data, Everywhere

Which regulation gives residents of the European Union control over how their personal data is collected and used?

  1. HIPAA
  2. PCI DSS
  3. SOX
  4. GDPR Correct answer.

Explanation

The General Data Protection Regulation governs the collection, use, and protection of personal data for people in the EU. HIPAA covers protected health information in the United States, PCI DSS covers card payment data, and SOX covers financial reporting.

Question 21Foundations: Data, Data, Everywhere

A researcher reads hundreds of open-ended survey responses and groups them into recurring themes such as price, service quality, and delivery speed. Which problem type does this illustrate?

  1. Identifying themes Correct answer.
  2. Spotting anomalies
  3. Discovering connections
  4. Making predictions

Explanation

Identifying themes means organizing qualitative, often text-based data into larger categories that capture what respondents are expressing. It differs from linking two variables or datasets, and from forecasting values or flagging unusual ones.

Question 22Foundations: Data, Data, Everywhere

In a data governance program, which responsibility typically belongs to a data steward?

  1. Appointing the board members who oversee data policy
  2. Deciding which products the company should launch
  3. Setting the company's overall legal accountability for data assets
  4. Maintaining data quality and usage standards day to day Correct answer.

Explanation

A data steward handles the day-to-day work of keeping data accurate, consistent, and properly used within an organization's governance framework. Legal accountability for data assets sits with data owners, and product or board decisions fall outside data stewardship.

Question 23Foundations: Data, Data, Everywhere

A hiring model trained on a company's past hiring decisions consistently ranks applicants from one demographic group lower. What best explains this outcome?

  1. The model was trained with too much metadata
  2. The applicants provided free-text responses
  3. The training data was too unstructured to use
  4. The model learned bias present in the historical data Correct answer.

Explanation

When historical data reflects past human bias, a model trained on it can reproduce and even amplify that bias in its outputs. Unstructured data, metadata volume, and free-text responses do not by themselves cause a systematic demographic disparity.

Question 24Foundations: Data, Data, Everywhere

A customer uses a company's mobile app and generates usage records. Within legal and policy limits, who generally owns those records?

  1. The company that collected the data Correct answer.
  2. The analytics team that analyzed it
  3. The individual customer who generated it
  4. The government of the country where the app is used

Explanation

Data ownership identifies who is accountable for and controls a data asset; in practice an organization that collects data through its own services typically owns it, subject to privacy laws and its stated policies. Customers, analyst teams, and governments do not hold that default role.

Question 25Foundations: Data, Data, Everywhere

Which statement about the six phases of the data analysis process is accurate?

  1. Analysts often revisit earlier phases as they learn more Correct answer.
  2. Each phase is completed exactly once, in strict order
  3. The Analyze phase is the only phase that involves data
  4. The process ends permanently once findings are shared

Explanation

The phases provide structure, but analysis is iterative: findings in Analyze or Share can send an analyst back to Ask or Prepare to refine the question or gather more data. Every phase involves data in some form, and the work usually continues into Act and beyond.

Question 26Foundations: Data, Data, Everywhere

A hospital keeps doctors' free-form progress notes as text files alongside lab results that are stored in neat rows and columns of a database. Which statement best describes these two data sources?

  1. Both sources are unstructured because they are captured by different hospital systems.
  2. Both sources are structured because they can be loaded into a single data warehouse.
  3. The progress notes are structured data, while the lab results are unstructured data.
  4. The progress notes are unstructured data, while the lab results are structured data. Correct answer.

Explanation

Structured data is organized into a predefined format such as rows and columns, which makes it easy to search and analyze; the lab results fit that description. Unstructured data has no fixed format and includes free text such as clinical notes. Storing data in one place does not change its underlying type.

Question 27Ask Questions to Make Data-Driven Decisions

According to the SMART framework for asking effective questions, what does the letter M represent?

  1. Measurable Correct answer.
  2. Motivational
  3. Manageable
  4. Modular

Explanation

SMART stands for Specific, Measurable, Action-oriented, Relevant, and Time-bound. A measurable question can be answered with concrete data rather than personal opinion.

Question 28Ask Questions to Make Data-Driven Decisions

Which of the following is the strongest SMART question for a marketing team to ask?

  1. Should we improve our marketing?
  2. Why is our brand not as popular as it used to be?
  3. How many subscribers did we gain each month this year? Correct answer.
  4. What do people in general think about marketing as an industry?

Explanation

A strong question names a specific metric, a defined population, and a measurable time window. The subscriber question specifies the metric and the period, so it can be answered with data.

Question 29Ask Questions to Make Data-Driven Decisions

A manager wants richer detail about why customers abandon checkout, so the question must invite explanation. Which question best fits that goal?

  1. Was checkout slow? Yes or no?
  2. What made you decide to stop at checkout? Correct answer.
  3. Do you agree that checkout is confusing?
  4. Did you abandon your cart because of shipping cost?

Explanation

Open-ended questions invite respondents to explain their reasoning in their own words, surfacing reasons the analyst may not have anticipated. Yes/no and 'do you agree' items restrict answers to a predefined set.

Question 30Ask Questions to Make Data-Driven Decisions

Which person is best described as a primary stakeholder on a data project?

  1. A reporter who later reads a summary
  2. The manager who requested the analysis Correct answer.
  3. An analyst in a different department
  4. A vendor who supplies the team's software

Explanation

A primary stakeholder both requests the work and directly acts on or is affected by the results. People who only read summaries or provide supporting services are secondary stakeholders.

Question 31Ask Questions to Make Data-Driven Decisions

Structured thinking is best described as which of the following?

  1. A process for organizing information to reveal gaps Correct answer.
  2. A statistical test for comparing two groups
  3. A way of presenting findings in slide decks
  4. A method for collecting as much raw data as possible

Explanation

Structured thinking is a deliberate process of recognizing the current situation, organizing what is known, revealing gaps, and identifying options. It concerns how reasoning is organized, not which tools are used.

Question 32Ask Questions to Make Data-Driven Decisions

Which item belongs in a scope of work (SOW) for a data project?

  1. A guarantee that the analysis will prove the hypothesis
  2. The full source code of the company's competitors
  3. The agreed deliverables, timeline, and milestones Correct answer.
  4. The personal contact details of every customer

Explanation

A scope of work defines what will be delivered, when, and at which milestones, together with roles and assumptions. It sets the boundaries of the engagement rather than promising outcomes or including unrelated material.

Question 33Ask Questions to Make Data-Driven Decisions

Which spreadsheet formula adds together the values in cells B2 through B15?

  1. =SUM(B2:B15) Correct answer.
  2. =ADD(B2:B15)
  3. =TOTAL(B2:B15)
  4. =B2+B15

Explanation

The SUM function adds a range of cells, and the colon in B2:B15 means every cell from B2 to B15 inclusive. Writing =B2+B15 would add only the first and last cells.

Question 34Ask Questions to Make Data-Driven Decisions

An analyst writes a formula containing $C$4 and then copies it down a column. What happens to that reference?

  1. It becomes C4 and shifts with the column
  2. It stays fixed on cell C4 Correct answer.
  3. It changes to $C$5, $C$6, and so on
  4. It converts to a named range automatically

Explanation

Dollar signs make a reference absolute, locking both the row and the column when the formula is copied. Without them, the reference shifts relative to the new position.

Question 35Ask Questions to Make Data-Driven Decisions

Which spreadsheet feature hides rows that do not meet a condition while keeping all the data in the sheet?

  1. Sorting
  2. Merging
  3. Filtering Correct answer.
  4. Freezing

Explanation

Filtering temporarily hides rows that fail a condition while leaving the underlying data intact. Sorting reorders rows instead of hiding them.

Question 36Ask Questions to Make Data-Driven Decisions

A column recording the number of support tickets each customer opened contains which type of data?

  1. Ordinal
  2. Qualitative
  3. Quantitative Correct answer.
  4. Textual

Explanation

Ticket counts are numbers that can be summed and averaged, which makes them quantitative. Qualitative data describes qualities or attributes rather than amounts.

Question 37Ask Questions to Make Data-Driven Decisions

Which question is a closed-ended question?

  1. Why did you contact support?
  2. How would you describe your experience?
  3. Did you renew your subscription? Correct answer.
  4. What led you to choose this plan?

Explanation

Closed-ended questions offer a limited set of responses, typically yes/no or a fixed scale, and are useful for confirming a specific fact. Open-ended questions invite the respondent to explain.

Question 38Ask Questions to Make Data-Driven Decisions

A survey item reads: 'Don't you agree that our new checkout is much faster?' What is the main problem with this question?

  1. It signals the expected answer Correct answer.
  2. It is too long for a survey page
  3. It allows more than one answer format
  4. It measures something unquantifiable

Explanation

A leading question embeds the answer the asker expects, pushing respondents toward agreement and distorting the data collected. Neutral wording such as 'How would you describe checkout speed?' avoids the problem.

Question 39Ask Questions to Make Data-Driven Decisions

When analysts examine the context of a dataset, which set of questions are they trying to answer?

  1. Who, what, where, when, why, and how Correct answer.
  2. Sort, filter, pivot, chart, and share results
  3. Mean, median, mode, range, and variance
  4. Plan, do, check, act, and review

Explanation

Context is established by asking who collected the data, what it describes, where and when it was collected, why, and how. These answers determine whether the data can address the business question.

Question 40Ask Questions to Make Data-Driven Decisions

An analyst finishes an analysis and must present it to decision-makers. What is the most useful way to frame the presentation?

  1. Share the raw data file without commentary
  2. Walk through every formula used in the spreadsheet
  3. Lead with findings and recommendations Correct answer.
  4. Focus on the difficulties encountered during cleaning

Explanation

Decision-makers need the conclusion and recommended action first, expressed in business terms they can act on. Technical detail belongs in an appendix for those who want it.

Question 41Ask Questions to Make Data-Driven Decisions

Which action is the clearest example of data-driven decision-making?

  1. Copying a competitor's pricing because it feels safer
  2. Choosing a supplier because the CEO has used them before
  3. Launching a product because the team is excited about it
  4. Adjusting opening hours after reviewing six months of sales data Correct answer.

Explanation

Data-driven decisions rest on evidence gathered for the purpose, such as patterns in historical sales records. Decisions based on habit, imitation, or enthusiasm are not grounded in the organization's own data.

Question 42Ask Questions to Make Data-Driven Decisions

A client keeps adding requests that were not part of the agreed scope of work. What is the most professional first response?

  1. Discuss the change and its impact on the plan Correct answer.
  2. Deliver the original scope late without telling anyone
  3. Refuse every request and end the engagement
  4. Complete the extra work quietly to protect the relationship

Explanation

Change requests are handled by making their impact visible: what the new work means for time, cost, and deliverables, followed by agreement on how to proceed. Silently absorbing extra work hides the trade-offs from everyone.

Question 43Ask Questions to Make Data-Driven Decisions

A project brief says only: 'Sales are bad.' Which rewrite best turns this into a usable problem statement?

  1. Why do customers dislike us?
  2. Online sales fell 12% in Q3 versus Q2 Correct answer.
  3. Everyone agrees sales are a problem
  4. Sales need to be better soon

Explanation

A usable problem statement names a measurable quantity, a comparison point, and a time frame. Vague statements and broad questions give no guidance on which data to collect.

Question 44Ask Questions to Make Data-Driven Decisions

An analyst needs to know how many orders in a list have a status of 'Cancelled'. Which function is designed for that task?

  1. =COUNTIF(C2:C100,"Cancelled") Correct answer.
  2. =AVERAGE(C2:C100)
  3. =SUM(C2:C100)
  4. =MAX(C2:C100)

Explanation

COUNTIF counts the cells in a range that meet a single condition, which is exactly what a status tally requires. SUM, AVERAGE, and MAX operate on numeric values and cannot evaluate a text criterion.

Question 45Ask Questions to Make Data-Driven Decisions

An analyst receives the request: 'Can you look at our website data?' Which follow-up question best narrows the request?

  1. Which colors do you prefer for the final report?
  2. Do you like the current dashboard layout?
  3. What decision will you make with the results? Correct answer.
  4. Should I use a pie chart or a bar chart here?

Explanation

Tying the request to a pending decision clarifies which metrics matter, the relevant time frame, and the level of detail required. Presentation questions come later, once the purpose is settled.

Question 46Ask Questions to Make Data-Driven Decisions

A dashboard shows average order value rising while total revenue falls. What is the most reasonable next step before recommending action?

  1. Conclude that the metric is broken and ignore it
  2. Report only the metric that looks better
  3. Check order volume and the time period Correct answer.
  4. Recommend raising prices immediately

Explanation

A rising average alongside falling revenue usually means the mix or number of orders changed, so volume and the comparison period must be examined before acting. Acting on one metric in isolation can lead to the wrong decision.

Question 47Ask Questions to Make Data-Driven Decisions

Which practice best keeps stakeholders engaged throughout a data project?

  1. Changing the project goal whenever a new idea appears
  2. Waiting until the final report before making first contact
  3. Sending raw spreadsheets without explanation
  4. Sharing regular progress updates at an agreed cadence Correct answer.

Explanation

Regular, predictable updates keep stakeholders aligned, surface problems early, and build trust in the work. Communication gaps tend to reappear later as unpleasant surprises.

Question 48Ask Questions to Make Data-Driven Decisions

A stakeholder asks: 'How can we make our app better?' Which version best converts this into a measurable, time-bound question?

  1. What should we change by Q4 to lift retention 5%? Correct answer.
  2. What do users generally think about our mobile app today?
  3. How can we make our app the best one in the whole market?
  4. Should we invest a lot more money in the app next year?

Explanation

A well-formed question specifies what will change, by when, and against which measurable target, which is what makes it testable with data. The other options are broad, opinion-based, or binary.

Question 49Ask Questions to Make Data-Driven Decisions

Which of the following is an example of qualitative data collected from customers?

  1. Monthly revenue in dollars
  2. Number of daily active users
  3. Average session length in seconds per user
  4. Customer comments in a feedback form Correct answer.

Explanation

Qualitative data captures qualities, opinions, and descriptions, such as written comments. Counts, durations, and currency amounts are numeric and therefore quantitative.

Question 50Ask Questions to Make Data-Driven Decisions

Which outcome is a genuine benefit of structured thinking?

  1. It breaks a large problem into manageable parts Correct answer.
  2. It makes spreadsheets run faster
  3. It removes the need to collect data
  4. It guarantees that the analysis will be completely correct

Explanation

Structured thinking makes an ambiguous problem tractable by decomposing it into parts that can be examined and solved in order. It supports analysis but does not replace data collection or guarantee results.