Apple Data Analyst Interview Questions + Guide in 2024

Apple Data Analyst Interview Questions + Guide in 2024Apple Data Analyst Interview Questions + Guide in 2024


Apple dramatically increased its employees in the last decade, reaching 161,000, demonstrating its position as one of the most trusted and popular consumer brands. To improve its products and deliver a better consumer experience, Apple relies on data analysts to enhance data models, develop reports, optimize existing workflows, and develop new ones.

Whether you have an upcoming interview for the Apple data analyst position or are just curious about the process, you’ll find everything you need here. We provide an overview of the interview process, ideal answers to past questions, and tips for acing the interview.

What Is the Interview Process Like for a Data Analyst Role at Apple?

Expect a multi-stage, intensive interview process. Depending on the seniority of the data analyst position you’re applying for, interviewers from different but related domains will rigorously evaluate your answers and give you feedback throughout a five-stage interview process incorporating multiple behavioral and technical rounds.

Submitting the Application

Unless you’re a genius in your field, don’t expect an Apple recruiter to reach out. Most likely, you’ll need to regularly comb the Apple Career Portal to find the right opportunity. Tailor your CV according to the job description and critical requirements for the data analyst role, and wait for a hybrid shortlisting process to accept your application. If accepted, an Apple representative will reach out to you, inviting you to the recruiter screening call.

Recruiter Screening Call

An Apple recruiter, possibly with your hiring manager, will conduct a 15 to 30-minute phone interview to verify your experience details and ask a few stimulating behavioral questions about your past projects, leadership skills, and communication abilities. Your hiring manager may ask brief technical questions to identify and confirm your skill level.

This is also your opportunity to ask about the data analyst role and Apple itself. After the round, you’ll be notified about their decision within a day or two.

Technical Phone Interview

If your hiring manager decides to move your application forward, you’ll meet with multiple interviewers through FaceTime in this virtual technical interview round. Typically, questions related to SQL, Python, data analysis concepts (including statistical analysis and hypothesis testing), and big data fundamentals are asked.

Take-Home Assignments

If successful in the previous stage, you will receive a take-home assignment to submit within 48 or 72 hours. You’ll likely receive a dataset related to Apple’s business, potentially on App Store purchases, user demographics, and device performance. Your task will involve exploring the data, understanding its structure, cleaning it, and further analyzing it according to the instructions. Visualization may also be a part of the task.

Proficiency in SQL and data manipulation will be assessed during this stage of the data analyst interview at Apple.

On-Site Interview Rounds

Progressing to the on-site interview rounds of the Apple data analyst role is a feat in itself. During the on-site visit, expect to meet the hiring manager and other potential team members over lunch.

In this day-long interview loop, be prepared for conversations with experts in various domains. During these one-on-one and multiple interviewer rounds, your technical and behavioral skills will be compared against those of other candidates.

If you’re successful in the data analyst interview at Apple, you’ll be notified by phone and email before pre-employment checks and the onboarding process.

What Questions Are Asked in an Apple Data Analyst Interview?

Modern data analyst positions are crucial for understanding consumer sentiments and making decisions accordingly. Therefore, assume you’ll be thoroughly evaluated for your technical and behavioral alignment with the role and Apple.

As a data analyst candidate, you can expect questions related to programming languages, SQL querying, ML models, analytics, and behavior.

We’ve hand-picked a few questions below that were previously asked in Apple data analyst interviews and have answered them to provide an in-depth picture of what to expect.

1. What are you looking for in your next job?

This question may be asked in an Apple data analyst interview to understand your career goals and whether they align with the company’s opportunities and culture.

How to Answer

Discuss your desire for meaningful work, opportunities for learning and growth, and alignment with Apple’s values and products. Avoid mentioning generic benefits or perks.


“I am seeking a role where I can apply my analytical skills to solve complex problems and contribute to impactful projects. I’m particularly drawn to Apple because of its commitment to innovation and its emphasis on creating products that enrich people’s lives. I’m excited about the opportunity to work on cutting-edge technologies and collaborate with talented teams to drive positive change.”

2. How do you approach resolving conflict with co-workers or external stakeholders, especially when you don’t really like them? Give an example of when you resolved a conflict with someone on the job.

Your interpersonal skills, conflict resolution abilities, and professionalism will be evaluated through this question. Apple wants to know how well you handle challenging situations and maintain positive relationships in a collaborative environment.

How to Answer

Describe a conflict resolution approach that emphasizes communication, empathy, and finding mutually beneficial solutions. Provide a specific example that demonstrates your ability to navigate conflicts effectively.


“I believe in addressing conflicts directly and respectfully, focusing on finding common ground and understanding the other person’s perspective. In a previous role, I disagreed with a team member over project priorities. Instead of escalating the issue, I scheduled a one-on-one meeting to discuss our concerns openly. Through active listening and constructive dialogue, we identified areas we could compromise in and agreed on a solution that satisfied both of us.”

3. How do you prioritize tasks and stay organized with multiple deadlines?

This question assesses your time management and organizational skills, crucial for handling complex projects.

How to Answer

Explain your approach to prioritization, including methods for assessing urgency, importance, and dependencies. Discuss tools or strategies you use to stay organized and meet deadlines efficiently.


“I prioritize multiple deadlines by first evaluating the urgency and impact of each task. I create a detailed timeline, breaking down projects into smaller, manageable tasks with deadlines. I use project management tools like Trello or Asana to track progress and set reminders. Additionally, I regularly communicate with stakeholders to manage expectations and adjust priorities as needed to finish projects on time without compromising quality.”

4. You’re a product manager at Meta. There is an upcoming stakeholder meeting in a few weeks, and they want you to suggest a new feature for Instagram Stories. What would this feature be, and what rationale would you give for choosing such a feature?

The interviewer for the data analyst role may ask this question to evaluate your product management skills, creativity, and ability to justify decisions based on user needs and business objectives.

How to Answer

Propose a compelling feature for Instagram Stories, supported by research or insights into user behavior and market trends. Clearly articulate the rationale behind your suggestion, emphasizing its potential impact on user engagement or business growth.


“I would propose implementing a ‘Collaborative Story’ feature for Instagram Stories, allowing multiple users to contribute to a single story thread. This feature capitalizes on the growing trend of social collaboration and encourages deeper engagement among users by enabling shared storytelling experiences. By encouraging cooperation, we can improve user interaction and retention, ultimately driving increased platform usage and ad revenue.”

5. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

Apple may ask this question to understand your capacity for innovation and willingness to go above and beyond in your work.

How to Answer

Describe a specific instance where you surpassed project goals or expectations, highlighting the actions you took and the impact of your contributions. Emphasize your proactive approach, creative solutions, and commitment to delivering high-quality outcomes.


“In a previous project, our team faced a significant technical challenge that threatened to delay our product launch. Seeing the urgency of the situation, I took the initiative to run extensive research and experimentation to identify alternative solutions. Through brainstorming sessions and rapid prototyping, we developed a novel approach that addressed the issue and improved overall product performance. As a result of our efforts, we not only met the project deadline but also exceeded performance benchmarks. Our work earned praise from both internal stakeholders and customers.”

6. To finish a class, students must pass four exams (exam ids: 1,2,3, and 4). Given a table exam_scores containing the data about all of the exams that students took, form a new table to track the scores for each student.

Note: Students took each exam only once.


For the given input:

student_id student_name exam_id score
100 Anna 1 71
100 Anna 2 72
100 Anna 3 73
100 Anna 4 74
101 Brian 1 65

the expected output should be:

student_name exam_1 exam_2 exam_3 exam_4
Anna 71 72 73 74


exam_scores table

Column Type
student_id INTEGER
student_name VARCHAR
exam_id INTEGER


Column Type
student_name VARCHAR
exam_1 INT
exam_2 INT
exam_3 INT
exam_4 INT

Data manipulation and transformation are critical tasks for data analysts working with databases at Apple. This question assesses your ability to pivot data in SQL, transforming rows into columns based on specific criteria.

How to Answer

To form a new table to track each student’s scores, you need to use SQL’s pivot functionality to transform rows into columns. You’ll pivot the data on the exam_id column and aggregate the scores for each exam.


SELECT student_name,
  SUM(IF(exam_id=1, score, NULL)) AS exam_1,
  SUM(IF(exam_id=2, score, NULL)) AS exam_2,
  SUM(IF(exam_id=3, score, NULL)) AS exam_3,
  SUM(IF(exam_id=4, score, NULL)) AS exam_4
FROM exam_scores
GROUP BY student_id;

7. As part of the financial management in a large corporation, the CFO wants to review the expenses in all departments for the previous financial year. Write an SQL query to calculate the total expenditure for each department. Additionally, for comparison purposes, return the average expense across all departments.

Note: The output should include the department name, the total expense, and the average expense (rounded to 2 decimal places). The data should be sorted in descending order by total expenditure.



departments table

Column Type

expenses table

Column Type
department_id INTEGER
amount FLOAT
date DATE


Column Type
department_name VARCHAR
total_expense FLOAT
average_expense FLOAT

Your SQL querying skills as a data analyst, particularly in performing calculations and aggregations across multiple tables, will be assessed through this question.

How to Answer

To calculate the total expenditure for each department and the average expense across all departments, you need to join the departments and expenses tables and perform aggregations.


WITH total_expense_by_dept AS
                  WHEN YEAR( = 2022 THEN e.amount ELSE 0
              END) AS total_expense
   FROM departments d
   LEFT JOIN expenses e ON = e.department_id
SELECT AS department_name,
       ROUND( AVG(total_expense) over () ,2) AS average_expense
FROM total_expense_by_dept te
ORDER BY total_expense DESC

8. Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half.

If there is no index where this condition is satisfied, return -1.

Example 1:


nums = [1, 7, 3, 5, 6]


equivalent_index(nums) -> 2

Example 2:


nums = [1,3,5]


equivalent_index(nums) -> -1

As a data analyst candidate at Apple, you’ll be expected to be able to analyze and derive insights from large datasets. This question tests your problem-solving and algorithmic skills.

How to Answer

Iterate through the list of integers, calculating the sum of the left half and the right half at each index. If the sums are equal, return the index. If no such index is found, return -1.


def equivalent_index(nums):
    total = sum(nums)
    leftsum = 0
    for index, x in enumerate(nums):
        # the formula for computing the right side
        rightsum = total - leftsum - x
        leftsum += x
        if leftsum == rightsum:
            return index
    return -1

9. Given an employees and departments table, select the top 3 departments with at least 10 employees and rank them according to the percentage of their employees making over $100,000.



employees table

Columns Type
first_name VARCHAR
last_name VARCHAR
salary INTEGER
department_id INTEGER

departments table

Columns Type


Column Type
percentage_over_100k FLOAT
department_name VARCHAR
number_of_employees INTEGER

This question evaluates your SQL querying skills in performing complex joins and aggregations.

How to Answer

Join the employees and departments tables, calculate the percentage of employees making over $100,000 for each department, and rank the departments accordingly.


SELECT AVG(CASE WHEN salary > 100000
        THEN 1 ELSE 0 END) AS percentage_over_100k
      , as department_name
      , COUNT(*) AS number_of_employees
FROM departments AS d
LEFT JOIN employees AS e
    ON = e.department_id

10. Write a function compute_deviation that takes in a list of dictionaries with a key and a list of integers and returns a dictionary with the standard deviation of each list.

Note: This should be done without using the NumPy built-in functions.



input = [
        'key': 'list1',
        'values': [4,5,2,3,4,5,2,3],
        'key': 'list2',
        'values': [1,1,34,12,40,3,9,7],


 output = {'list1': 1.12, 'list2': 14.19}

Apple may ask this question to evaluate your ability to implement mathematical algorithms and handle data analysis tasks efficiently.

How to Answer

Iterate through the list of dictionaries and calculate the standard deviation for each list of integers using the formula for standard deviation without using NumPy functions.


def compute_deviation(input):
    result = {}
    for data in input:
        key = data['key']
        values = data['values']
        mean = sum(values) / len(values)
        variance = sum((x - mean) ** 2 for x in values) / len(values)
        stddev = variance ** 0.5
        result[key] = round(stddev, 2)
    return result

11. Imagine you’re given an Apple product review dataset with mixed formats (e.g., some dates as “yyyy-mm-dd”, others as “dd/mm/yyyy”). Describe the steps you’d take in Python to clean and standardize the date format for further analysis.

Data cleaning is critical for analysts when dealing with real-world datasets at Apple that often contain format inconsistencies. This question assesses your ability to handle data cleaning and standardization tasks using Python.

How to Answer

Describe the steps for data manipulation and parsing each data string into a consistent format using libraries like pandas.


“First, I’d import the pandas library and load the Apple product review dataset into a DataFrame. Then, I’d iterate through the date columns, converting each date string into a datetime object using the pd.to_datetime() function with appropriate format specifiers for each date format encountered. For instance, for dates in ‘yyyy-mm-dd’ format, I’d use format=‘%Y-%m-%d’, and for dates in ‘dd/mm/yyyy’ format, I’d use format=‘%d/%m/%Y.’ Finally, I’d overwrite the original date columns with the standardized datetime objects.”

12. You have a dataset of App Store user purchase history. How would you use Python libraries like pandas to segment users into different categories based on their purchase behavior (e.g., frequent buyers, specific product preference)?

This question evaluates your proficiency in using Python libraries like pandas, as a data analyst, for data segmentation based on specific criteria.

How to Answer

Explain how you would import the necessary libraries and load the dataset into a pandas DataFrame. Mention identifying relevant features for segmentation. Describe using pandas functions and methods to group users based on these features and define segmentation criteria.


“To segment users based on their purchase behavior from the App Store purchase history dataset using Python and pandas, I’d start by importing the pandas library and loading the dataset into a DataFrame. Then, I’d identify relevant features such as purchase frequency, product preferences, or total spending. Using the pandas groupby() function, I’d group users based on these features, for example, grouping frequent buyers or users with specific product preferences into separate segments. Finally, I’d analyze each segment to understand the characteristics and behaviors of different user groups.”

13. Discuss strategies for prioritizing analysis and identifying key insights without processing the entire dataset at once.

The data analyst interviewer at Apple may ask this to explore your strategic thinking in handling large datasets efficiently.

How to Answer

Explain that you would prioritize analysis by first understanding the business objectives and identifying the most critical questions to be answered. Then, you’d focus on subsets of the data most relevant to these objectives. Additionally, explain how you’d leverage exploratory data analysis (EDA) techniques to guide further analysis.


“I would start by understanding the business goals and identifying the key questions that need to be addressed through data analysis. Then, I’d prioritize analysis by focusing on subsets of the dataset most relevant to these questions, either through sampling methods or by filtering based on specific criteria. Additionally, I’d use exploratory data analysis techniques to uncover initial insights and refine my approach iteratively as I move deeper into the data.”

14. Describe an SQL approach to estimate the CLTV for Apple customers, considering factors like purchase history and product type.

This question will evaluate your ability to leverage SQL to analyze customer data and derive insights crucial for business strategy and decision-making at the data analyst interview at Apple.

How to Answer

Discuss an approach that involves querying relevant tables to calculate metrics like average purchase value, purchase frequency, and customer lifespan. Then, use SQL functions and aggregations to calculate CLTV based on these metrics.


“To estimate the CLTV for Apple customers using SQL, I would first query relevant tables such as customer transactions and product data. Then, I’d calculate key metrics such as average purchase value, purchase frequency, and customer lifespan using SQL functions and aggregations. Finally, I’d utilize these metrics to calculate CLTV, considering factors like historical purchase behavior and product preferences.”

15. Apple is considering a new fitness tracking feature for the Apple Watch. How would you design an A/B test using machine learning to evaluate its impact on user engagement?

This question checks your ability to design and implement an A/B test using machine learning techniques to evaluate a new feature’s impact on user engagement.

How to Answer

Explain that you would start by defining clear objectives and hypotheses for the A/B test. Then, you’d split users into control and treatment groups. Next, you’d design features to represent the new fitness tracking feature and deploy machine learning models. Finally, you’d analyze the results using appropriate statistical techniques.


“To design an A/B test for evaluating the impact of a new fitness tracking feature on user engagement, I would first define clear objectives and hypotheses, specifying the primary metric for measuring engagement, such as daily active users or session duration. Then, I’d split users randomly into control and treatment groups, ensuring balance between groups to minimize biases. Next, I’d design features to represent the new feature’s functionality and deploy machine learning models, such as logistic regression or decision trees, to predict user engagement based on these features. Finally, I’d analyze the results using statistical techniques like hypothesis testing or confidence intervals to determine the feature’s impact on user engagement.”

16. Explain how machine learning models like decision trees or survival analysis could be used to predict when iPhone users are likely to upgrade to a new model.

User sentiment prediction is essential to being a data analyst at Apple. Your interviewer may ask this question to evaluate your understanding of machine learning models and their application in predicting user behavior.

How to Answer

Explain the basic principles behind decision trees or survival analysis and how they can be applied to predict iPhone upgrade patterns. Discuss the relevance of data features in training the model to make accurate predictions.


“Machine learning models like decision trees or survival analysis can be used to predict when iPhone users are likely to upgrade to a new model by analyzing various factors such as usage patterns, purchase history, and device lifespan. Decision trees split the data based on features like usage time, app downloads, and previous upgrade cycles to create a predictive model. Survival analysis, on the other hand, considers time-to-event data, such as the duration between previous upgrades and the likelihood of future upgrades based on similar user profiles.”

17. Describe the potential challenges and steps involved in building a machine learning model to identify and remove fake reviews from the App Store.

Apple may ask this question to assess your problem-solving skills and ethical considerations regarding user-generated content in the App Store.

How to Answer

Discuss potential challenges in identifying fake reviews. Explain the steps involved in data preprocessing and highlight the importance of model interpretability and continuous monitoring to ensure effectiveness and fairness.


“Building a machine learning model to identify and remove fake reviews from the App Store involves several challenges. First, acquiring labeled data for training can be difficult due to the subjective nature of fake reviews. Second, selecting relevant features from text data, such as sentiment analysis and linguistic patterns, requires careful consideration. Additionally, model evaluation must account for imbalanced classes and evolving strategies used by fake reviewers. Preprocessing steps like text tokenization and feature extraction are essential, along with regular model updates to adapt to new patterns of fake reviews.”

18. Explain how you’d write an SQL query to recommend related products to users based on their past purchases using a join operation.

This question assesses your SQL proficiency and understanding of relational databases in recommending related products to users.

How to Answer

Explain how to write an SQL query using join operations to retrieve related products based on users’ past purchases. Discuss the importance of understanding database schema and using appropriate join conditions to link user and product tables effectively.


“To recommend related products to users based on their past purchases, I would write an SQL query that joins the user’s purchase history table with the product catalog table. By linking the tables using common identifiers such as user ID and product ID, the query can retrieve products that are frequently purchased together or share similar attributes. Utilizing inner or outer joins depending on the desired recommendation strategy, the SQL query selects relevant products to suggest to users, improving their shopping experience.”

19. Imagine we have a massive dataset containing information about iPhone app user behavior. Each record includes details like app usage time, location data, and in-app purchase history. How would you approach identifying which users are most likely to make in-app purchases, considering the size and complexity of the data?

The data analyst interviewer at Apple will evaluate your approach to analyzing large and complex datasets to identify user behavior patterns with this question.

How to Answer

Discuss strategies for analyzing user behavior data to identify users most likely to make in-app purchases. Highlight the importance of feature engineering and model selection in handling the size and complexity of the dataset effectively.


“When faced with a massive dataset containing information about iPhone app user behavior, I would approach identifying users most likely to make in-app purchases by first performing exploratory data analysis to understand patterns and trends. Using techniques like segmentation based on usage time, location data, and in-app purchase history, I would then employ predictive modeling methods such as logistic regression or random forest to identify predictors of in-app purchases. Feature engineering that includes creating new variables or transforming existing ones would be crucial in capturing relevant user behavior signals for accurate predictions.”

20. Explain the thought process behind choosing between a bar chart and a scatter plot for visualizing a dataset comparing iPhone model sales across different countries.

Your answer to this question will show if you can effectively communicate insights through visualizations to fuel strategic decision-making processes as a data analyst at Apple.

How to Answer

Explain the considerations in choosing between a bar chart and a scatter plot for visualizing iPhone model sales data across different countries. Discuss the strengths and weaknesses of each visualization type in highlighting sales trends and relationships between variables.


“In visualizing a dataset comparing iPhone model sales across different countries, the choice between a bar chart and a scatter plot depends on the nature of the data and the insights we’re trying to convey. A bar chart would be suitable for comparing sales volumes of different iPhone models across countries, providing a clear representation of relative sales performance.

On the other hand, a scatter plot could be more effective in illustrating the relationship between iPhone model sales and additional variables such as price or market share. By plotting sales data points for each country, a scatter plot allows for identifying patterns or outliers that may not be clear in a bar chart, providing deeper insights into market dynamics.”

How to Prepare for the Data Analyst Interview at Apple

There’s no secret recipe for success in the Apple data analyst interview except practice and our Data Analyst Interview Guide. So, digilently prepare for each stage of the process to gain a competitive advantage. Here’s what to do:

Understand Apple’s Culture and Values

Research Apple’s mission, values, and culture to align your responses with their beliefs. Tailor your answers to behavioral questions according to them. Stay updated with Apple’s products, services, and news. When the opportunity arises, demonstrate your interest in joining the company.

Practice Programming languages

Expect questions from algorithmic concepts, problem-solving scenarios, and programming languages. Prepare answers for questions about Python libraries, ML model development, and data analysis tools like SQL and Excel.

To prepare further, follow our SQL Learning Path and Python Course to better understand coding problems.

Brush Up on Data Analysis Concepts

Refresh your data analysis skills, including cleaning, manipulation, visualization, and interpretation. Practice solving data analysis problems using real-world case studies. Moreover, familiarize yourself with common statistical methods and machine learning algorithms.

Prepare for Behavioral and Technical Questions

Solve as many data analyst behavioral interview questions as possible before the first round of Apple interviews. Also, be well-prepared by answering 100+ data analyst interview questions designed to challenge your technical prowess.

Participate in Mock Interviews

Challenge yourself and refine your responses by participating in our peer-to-peer mock interview sessions. Also, improve your answers by submitting them to our AI-assisted Interview Mentor Program.


How much do data analysts at Apple earn in a year?


Average Base Salary


Average Total Compensation

Min: $61K
Max: $150K
Base Salary
Median: $91K
Mean (Average): $98K
Data points: 104
Min: $163K
Max: $218K
Total Compensation
Median: $191K
Mean (Average): $191K
Data points: 2

View the full Data Analyst at Apple salary guide

Apple data analysts, on average, make $98,000 in base salary and $190,000 in total compensation. However, the max base salary and compensation are significantly higher and can be earned by upskilling and showing dedication to your work. Follow our data analyst salary guide to gain insight into the industry standards.

What other companies can I work at as a data analyst besides Apple?

Data analysts are indispensable in virtually every contemporary organization with a sizable customer base. Companies like Microsoft, Airbnb, and Google stand out as potential alternatives, offering varied opportunities tailored to your skill level and personal interest.

Are there job postings for Apple data analyst roles available on Interview Query?

Yes, the latest job postings for Apple data analyst roles are available on our job board. However, regularly check the official Apple career page for more updated info on the latest opportunities.

The Bottom Line

Succeeding in a data analyst interview at Apple requires a robust foundation in coding, data manipulation techniques, and SQL querying. Machine learning model questions may also be asked during the technical rounds. As well, prepping with behavior questions would contribute to your overall performance in the interview.

Regardless, don’t obsess over a particular company and position. Check out our Company Interview Guide to consider other establishments that hire data analysts. If you’re looking for other jobs with Apple, check out our main Apple interview guide, which includes data engineer, data scientist, software engineer, and machine learning engineer roles.