PDF Version DemoThree versions, one standard of quality: the IBM Assessment: IBM Machine Learning Data Scientist v1 package from ActualPDF comes as a printable PDF, a Windows test engine, and an online version that candidates increasingly prefer for studying on any device. All carry the same A1000-144 questions.
IBM A1000-144 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | Assessment: IBM Machine Learning Data Scientist v1 |
| Exam Number: | A1000-144 / C1000-144 |
| Exam Format: | Scenario-based, Multiple-choice |
| Real Exam Qty: | 61 |
| Exam Price: | $200 USD |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Passing Score: | 74% (45 out of 61) |
| Related Certifications: | IBM Certified Data Scientist - Watson Specialist v1 |
| Certificate Validity Period: | 2 years |
| Recommended Training: | IBM Machine Learning with Watson Studio IBM Learning Path: Data Scientist |
| Exam Registration: | IBM Certification Portal Pearson VUE Registration |
| Exam Way: | Online proctored or at authorized Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended: basic Python/R, SQL, statistics, and Watson Studio experience |
| Official Syllabus URL: | https://www.ibm.com/certify/certifications/c1000-144 |
IBM A1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitor and Maintain Models in Production | 10% | - Update and retrain models as needed - Assess model performance and accuracy - Detect model drift and data drift - Identify and mitigate bias and fairness issues |
| Select and Implement Machine Learning Models | 25% | - Unsupervised learning: Clustering algorithms - Unsupervised learning: Dimensionality reduction - Model selection criteria and trade-offs - Supervised learning: Regression techniques - Supervised learning: Classification techniques |
| Evaluate Business Problem and Ethical Considerations | 20% | - Understand business requirements and objectives - Identify available data sources and constraints - Assess ethical, legal, and compliance implications - Apply AI design thinking and AI Ladder framework |
| Exploratory Data Analysis and Data Preparation | 25% | - Handle missing values and outliers - Clean, label, anonymize, and transform data - Balance, partition, and split datasets - Perform statistical analysis and data visualization |
| Refine, Optimize and Deploy Models | 20% | - Model explainability and interpretability - Feature engineering and feature selection - Use IBM Watson Studio and related tools - Hyperparameter tuning and model optimization - Prepare environment for model deployment |
IBM A1000-144 Exam: What Candidates Want to Know
IBM Assessment: IBM Machine Learning Data Scientist v1 is an official IBM certification exam, registered under the code A1000-144. Passing it awards the IBM Certified Data Scientist - Machine Learning Specialist v1 certification, a credential at the Intermediate / Specialist level. It also connects to IBM Certified Data Scientist - Watson Specialist v1. The exam is demanding by design, and that difficulty is precisely what makes the credential meaningful for career development.
The IBM Assessment: IBM Machine Learning Data Scientist v1 exam presents 61 questions within 90 minutes. That is a brisk pace, and the candidates who handle it best are the ones who rehearsed it. Use the ActualPDF engine for full timed simulations, practice flagging and returning, and arrive on exam day with a pacing strategy already proven.
Passing IBM Assessment: IBM Machine Learning Data Scientist v1 takes 74% (45 out of 61), and official registration costs $200 USD. Retakes bill the full $200 USD again, so preparation is the least expensive insurance available. Let your ActualPDF practice scores guide the timing: book when you clear the requirement consistently, not occasionally.
No mandatory prerequisites; recommended: basic Python/R, SQL, statistics, and Watson Studio experience
Policies get revised, so confirm the current requirements before you register on the official exam page.
IBM Assessment: IBM Machine Learning Data Scientist v1 registration is handled through the official channels below.
For scheduling purposes: the exam is delivered Online proctored or at authorized Pearson VUE test centers.
Yes, IBM recommends the following training for IBM Assessment: IBM Machine Learning Data Scientist v1 candidates.
Complement any training with the 0 practice questions in the ActualPDF A1000-144 package, because repeated application is what turns course knowledge into a passing score.
Yes. ActualPDF offers a free demo of the IBM Assessment: IBM Machine Learning Data Scientist v1 questions, so you can verify the quality personally before purchasing. Your purchase then includes a one-year service warranty: updates are free for 365 days, and after expiry you can extend the update service at a 50% discount.
Your money is protected by a 100% money-back guarantee with defined conditions. Take the IBM Assessment: IBM Machine Learning Data Scientist v1 exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. You may instead wait for the update version or change to other exam material: exchange for two other exam products of equal value, free, with your original purchase keeping its update service.
Delivery is instant: files unlock for download at payment and are emailed within one minute. If nothing arrives within 2 hours, check spam and contact customer service, online 7/24 even on official holidays. Installation is unlimited across your computers.
IBM Assessment: IBM Machine Learning Data Scientist v1 is organized into 5 official domains. The most heavily weighted are Refine, Optimize and Deploy Models (20%), Monitor and Maintain Models in Production (10%), and Exploratory Data Analysis and Data Preparation (25%). The full breakdown appears above on this page; study the weightings and your preparation priorities set themselves.
