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Readiness test · Free · No sign-up required

Data Science Readiness Score

Working toward AWS MLA-C01, Azure AI-300, DP-750, or a data science interview? This free check covers the NumPy, Pandas, statistics, ML evaluation, and feature engineering topics that every data science exam tests. Get an instant score and a topic-by-topic breakdown of where to focus.

One question at a time, explained as you go · no sign-up

Instant resultsTopic breakdownWeak area detectionPass likelihood

What this test covers

Data science exams and technical interviews converge on the same core material: NumPy array operations, Pandas DataFrames and groupby aggregations, descriptive and inferential statistics, machine learning fundamentals (bias-variance, cross-validation), model evaluation metrics (precision, recall, F1, AUC-ROC), and feature engineering techniques. This free readiness check samples across all six areas so you get a topic breakdown, not just a pass/fail. Score 85 or above and you are ready. Between 70 and 84, identify the one or two weak topics and tighten them. Below 70, the AI Report ($19) builds a personalised study plan targeting your specific gaps.

  • NumPy — array operations, broadcasting, vectorisation
  • Pandas — DataFrames, GroupBy, merge, time series
  • Statistics — distributions, hypothesis testing, A/B testing
  • Machine Learning — bias-variance, cross-validation, regularisation
  • Model Evaluation — precision, recall, F1, ROC-AUC, confusion matrix
  • Feature Engineering — encoding, scaling, imputation, feature selection

How it works

01

Pick your difficulty

Choose Easy, Medium, or Full test depending on where you are in your learning.

02

Answer questions

One question at a time. Click an answer and get instant feedback with a full explanation.

03

Get your score

See your readiness score (0–100), pass likelihood, and a topic-by-topic breakdown.

Frequently asked questions

What exams is this readiness check relevant for?
This check covers the core data science and ML material tested on AWS ML Engineer Associate (MLA-C01), Azure AI-300 MLOps Engineer, Azure DP-750 Databricks, and general data science interviews. The NumPy, Pandas, statistics, and model evaluation topics appear on all of these.
How is this different from the AWS ML Engineer readiness check?
The AWS ML Engineer check (/score/aws-ml-engineer) is scoped specifically to MLA-C01 objectives: SageMaker, ML pipelines, MLOps, and AWS-specific tooling. This data science check covers the Python-first fundamentals — NumPy, Pandas, statistics — that underpin multiple certifications and data science interviews.
What score means I am ready?
A score of 85-100 is Exam Ready. 70-84 is Almost Ready — review your weakest one or two topics. Below 70, the AI Report ($19) maps a personalised study plan to your exact weak areas and the specific exam objectives you need to cover.
How many questions are in this readiness check?
The full test (Hard mode) has up to 30 questions across six topics. Easy mode focuses on foundational NumPy, Pandas, and statistics; Hard mode adds inference, evaluation trade-offs, and engineering-style questions.
Is this free?
Completely free — no account or credit card required. After your score you can optionally purchase the AI Report ($19) for a personalised study plan targeting your specific weak areas.
Does Azure DP-100 still exist?
Azure DP-100 (Azure Data Scientist Associate) was retired on June 1, 2026. It was replaced by AI-300 MLOps Engineer Associate and DP-750 Implementing Data Science with Microsoft Fabric and Databricks. If you were studying for DP-100, your statistics and ML fundamentals carry over fully to both new exams.
What if I score below 70? Is a tutor the right next step?
A score below 70 usually means one or two topic areas need focused attention rather than a full restart. The AI Report ($19) identifies your specific gaps. If you prefer live instruction, the results page links to 1-on-1 data science tutors on Wyzant who specialise in exam prep.

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