Alteryx Machine Learning Fundamentals Micro-Credential and how to pass it

by Eugenia Losada Gamst

This week I attempted the Alteryx Machine Learning Fundamentals Micro-Credential, and while preparing for it I realized that there was not really many resources to help you prepare for the exam. For this reason here I have compiled all my tips and the resources I used to prepare and get a 90% pass score first try with 48hr prep.

Exam info:

  1. 1hr long
  2. 40 questions
  3. Only theory (no practical questions in Alteryx)
  4. 1 attempt every 7 days
  5. Open book (you can use google during the exam)
  6. Here is the prep guide with a few more details
Machine Learning Fundamentals Micro-Credential Exam Prep Guide
Download the Machine Learning Fundamentals Micro-Credential Exam Prep Guide

Exam outline:

Machine Learning Concepts: 40%

Data Preparation and Analysis: 35%

Evaluation and Interpretation of Models: 25%

Resources I used:

  • This video from Alteryx community:
Defining Data
Lesson Objective: Ease into a deeper understanding what counts as data. Estimated Time: ~36 Minutes Description: Examine how data is organized. Review common file types used in data analytics. Explore data types. Realize the value of metadata. Understand why visualization is key to sharing your fin…
  • Alteryx machine learning practice test: At the end of each video of the machine learning interactive lessons linked there are quizzes I completed to get an idea of the kinds of questions that would be asked on the exam. I recommend watching the lessons but if you try the tests first and get most of them right there is no need for this.
Interactive Lessons
Dive into new analytics techniques with lessons that incorporate videos, hands-on activities and quizzes to assess your knowledge. Also available in | Français | Português | Español | 日本語

Exam contents you need to know:

I realized there were a few topics that were coming up more than expected so here is a list of subjects included in the exam outline that you need to pay special attention to:

  • Alteryx data science cycle: SUPER IMPORTANT! This came up multiple times so make sure you know it. The cycle is the following (attached image at the end of list) including model monitoring after all the steps
  • Standardization VS normalization
  • What to do if data is missing: Methods such as interpolating, deleting mostly null rows, excluding variables etc...
  • Linear Regression, Time series and Classification
  • Cross validation
  • Supervised learning
  • Descriptive statistics
  • Independent and dependent variable categorization
  • ML Alteryx products
  • Regression and time series (Numerical target) & Classification and clustering (Category target)
  • Overfitting & Underfitting


This test was easier to pass than I had expected, but it was a little frustrating that it was hard to find resources to help me prepare for it. However, thanks to the open book policy if there were any very niche question that came up I could just google for quick reads on the topic to help me figure out the answerer. I believe someone with no prior machine learning experience could pass the exam first try as long as they complete the interactive lessons and are good googlers, so good luck to anyone planning to take this certification!