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Quizzes > Quizzes for Business > Technology

Ace the Data Analyst Technical Assessment Quiz

Test Your Data Analysis Knowledge and Skills

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art representing a Data Analyst Technical Assessment Quiz.

Ready to sharpen your data analysis expertise? This Data Analyst Technical Assessment Quiz challenges you with realistic data tasks and scenarios so you can gauge your current proficiency. Ideal for aspiring analysts and seasoned professionals alike, this practice quiz covers SQL, data visualization, and statistical reasoning. After completing the quiz, expand your toolkit by exploring our Data Analyst and Engineer Skills Assessment Quiz or dive into broader Technical Skills Assessment Quiz options. Feel free to customize all questions in our editor and browse more quizzes to tailor your learning journey.

Which measure of central tendency is least affected by outliers in a dataset?
Median
Mean
Range
Mode
The median represents the middle value and remains unchanged by extreme values. This makes it robust to outliers compared to the mean.
Which SQL query retrieves all columns from the table named employees?
SELECT * FROM employees;
SELECT employees.* FROM employees;
SELECT columns FROM employees;
SELECT ALL employees;
The asterisk in SELECT * indicates that all columns should be returned from the specified table. This is the standard SQL syntax for retrieving all fields.
Which chart is most appropriate for displaying the distribution of a single numerical variable?
Bar chart
Scatter plot
Histogram
Pie chart
A histogram shows the frequency distribution of continuous numerical data by grouping values into bins. It is specifically designed to display data distribution patterns.
Which pandas function removes duplicate rows from a DataFrame?
drop_duplicates()
merge()
fillna()
concat()
The drop_duplicates() method identifies and removes duplicate rows in a pandas DataFrame. This is the standard approach to eliminate repeated records.
Which KPI measures the average revenue generated per user or customer?
Churn Rate
Customer Lifetime Value
Net Promoter Score
Average Revenue Per User
Average Revenue Per User (ARPU) calculates the mean revenue generated by each user or customer over a specific period. It directly quantifies revenue efficiency on a per-customer basis.
What does a correlation coefficient of -0.8 indicate between two variables?
Moderate positive relationship
No linear relationship
Weak positive relationship
Strong negative relationship
A correlation coefficient of -0.8 indicates a strong inverse relationship, meaning that as one variable increases, the other tends to decrease significantly. Values close to -1 represent strong negative correlations.
Which SQL query correctly groups sales by region and calculates total sales?
SELECT region, SUM(sales) FROM orders;
SELECT SUM(sales) FROM orders GROUP BY region;
SELECT region, SUM(sales) FROM orders GROUP BY region;
SELECT region, sales FROM orders GROUP BY region;
Using GROUP BY region with SUM(sales) returns the total sales per region. Including both the grouping column and the aggregate function in the SELECT clause ensures valid grouping.
Which imputation method is most appropriate for missing values in a categorical column?
Principal component imputation
Mean imputation
Mode imputation
Median imputation
Mode imputation replaces missing categorical values with the most frequent category. This preserves the existing distribution of categories better than numerical methods.
Which visualization is best for showing the relationship between two continuous variables?
Histogram
Pie chart
Scatter plot
Bar chart
A scatter plot displays two continuous variables on orthogonal axes, allowing observation of patterns or correlations. Other charts like histograms or bar charts are better for single-variable distributions or categories.
Which SQL join returns all customers and their orders, including customers with no orders?
INNER JOIN
RIGHT JOIN
FULL JOIN
LEFT JOIN
A LEFT JOIN returns all rows from the left table (customers) and matches rows from the right table (orders), filling with nulls when there is no match. This ensures every customer appears regardless of orders.
Which KPI measures the efficiency of converting website visitors into customers?
Churn Rate
Conversion Rate
Customer Acquisition Cost
Net Profit Margin
Conversion rate measures the percentage of visitors who complete a desired action, reflecting the efficiency of turning leads into customers. It directly gauges the effectiveness of marketing and sales funnels.
In regression analysis, what does the R-squared value represent?
Proportion of variance explained by the model
Total number of data points
Residual error magnitude
Correlation between predictors
R-squared indicates the fraction of the variance in the dependent variable that is predictable from the independent variables. A higher R-squared value shows better explanatory power of the model.
What is the primary purpose of data normalization in preprocessing?
Reducing the number of features
Scaling features to a common range
Removing outliers
Encoding categorical variables
Data normalization transforms numerical features to a common scale, preventing features with larger ranges from dominating analysis. It is critical for algorithms sensitive to feature magnitude.
Which SQL function returns the total number of rows in a table?
MAX()
SUM()
AVG()
COUNT(*)
COUNT(*) tallies all records in a table or result set, returning the total row count. Other functions like SUM and AVG perform mathematical calculations on column values.
Which data cleaning action addresses inconsistent date formats?
Convert dates to uppercase
Fill missing dates with zero
Drop the date column entirely
Standardize dates to a consistent format
Standardizing date formats ensures consistency across records and enables proper parsing and comparison. Converting dates to an ISO format like YYYY-MM-DD is a common best practice.
Which SQL window function computes a running total of sales ordered by date?
ROW_NUMBER() OVER (ORDER BY date)
RANK() OVER (ORDER BY date)
SUM(sales) OVER (ORDER BY date)
LAG(sales) OVER (ORDER BY date)
The SUM() window function with an OVER clause and ORDER BY calculates a cumulative running total of sales in date order. Other functions like ROW_NUMBER or LAG serve different purposes.
When using regression for missing data imputation, what assumption must hold?
Data are missing completely at random
No multicollinearity among predictors
Data are missing at random
Homoscedasticity of errors
Regression-based imputation assumes that data are missing at random (MAR), meaning the missingness depends only on observed values. Violating this assumption can bias imputed estimates.
In a scatter plot matrix of multiple features, what issue is most readily identified?
Multicollinearity
Heteroscedasticity
Autocorrelation
Seasonality
A scatter plot matrix visualizes pairwise relationships among variables, revealing strong linear correlations indicative of multicollinearity. Identifying such dependencies helps in feature selection and model building.
What is a key drawback of using dual y-axes in a single visualization?
Misinterpretation of scale
Faster rendering
Reduced data distortion
Improved clarity
Using dual y-axes can confuse readers as different metrics may have incompatible scales, leading to misleading comparisons. It is generally discouraged unless scales and context are clearly explained.
Which KPI best evaluates the long-term value a customer brings to a business?
Customer Lifetime Value
Average Order Value
Churn Rate
Bounce Rate
Customer Lifetime Value predicts the total revenue a business can expect from a customer over the entire relationship. It focuses on long-term profitability unlike short-term measures like average order value.
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Learning Outcomes

  1. Analyse datasets using statistical methods to derive insights.
  2. Master interpretation of data patterns and trends to guide decisions.
  3. Apply SQL queries to retrieve and manipulate data effectively.
  4. Demonstrate proficiency in data visualization tools and techniques.
  5. Evaluate data cleaning strategies to ensure dataset accuracy.
  6. Identify key performance indicators relevant to business objectives.

Cheat Sheet

  1. Descriptive vs Inferential Statistics - Think of descriptive statistics as the ultimate summary party for your data, where you get the mean, median, and mode all in one place. Inferential statistics then swoop in like a detective, using a sample to draw big-picture conclusions about a whole population. Learn more
  2. Parametric vs Nonparametric Tests - Parametric tests assume your data is well-behaved (hello, normal distribution!), while nonparametric tests are the free spirits that don't mind weird or skewed data. Knowing when to pick each is like choosing the right tool - use the one that fits your data's personality. Read the full article
  3. Mastering Basic SQL Queries - SELECT, WHERE, and JOIN are your SQL building blocks - use them to retrieve and combine data like a pro chef mixing ingredients. Practice these commands to slice and dice multiple tables into the exact dataset you need. Explore examples
  4. Advanced SQL with Window Functions - Window functions let you calculate running totals, moving averages, and rank data without collapsing rows - imagine getting instant side-by-side comparisons for every record. They're perfect for time series analysis or leaderboard-style reports. Dive into techniques
  5. Data Visualization Magic - Turn raw numbers into eye-catching charts and graphs that tell a story at a glance - think bar plots for comparisons or scatterplots for spotting clusters. Strong visuals help you and your audience spot trends, outliers, and patterns faster than a spreadsheet ever could. Get inspired
  6. Essential Data Cleaning Strategies - Cleaning data is like giving your dataset a spa day: handle missing values, remove duplicates, and fix inconsistent entries so your analyses run smoothly. A little tidying up can prevent big headaches later and ensure your insights are rock-solid. Learn the techniques
  7. Identifying Key Performance Indicators (KPIs) - KPIs are your scoreboard metrics - they show how close you are to hitting business goals like revenue targets or customer satisfaction. Pick KPIs that align with objectives and you'll always know if you're winning or need a new game plan. Read more
  8. Importance of Exploratory Data Analysis (EDA) - EDA is your first date with a new dataset: you'll summarize, visualize, and poke around to uncover hidden patterns and sanity-check assumptions. It's the foundation for any solid analysis and helps steer your project in the right direction. Discover EDA
  9. SQL Join Types Demystified - INNER JOIN finds exact matches, LEFT JOIN keeps all rows from the left table, and RIGHT JOIN keeps all rows from the right - think of them as different ways to shake hands between tables. Mastering joins is key to combining data tables with finesse. See examples
  10. Complex SQL: Subqueries, CTEs & Set Operators - Level up your queries by nesting subqueries, using Common Table Expressions (CTEs) for clarity, and merging results with UNION or INTERSECT. These advanced moves let you tackle multi-step analyses in a single, elegant script. Learn how
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