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IBM C1000-154 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM Watson Data Scientist v1 |
| Exam Number: | C1000-154 |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Exam Price: | $200 USD |
| Passing Score: | 63% |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 61 |
| Related Certifications: | IBM Applied AI Professional Certificate IBM Data Science Professional Certificate |
| Exam Format: | Multiple Response, Multiple Choice |
| Sample Questions: |  |
| Exam Way: | Online proctored or at authorized testing center |
| Pre Condition: | Recommended: IBM Data Science Professional Certificate or equivalent experience with IBM Cloud and data science tools |
| Official Syllabus URL: | https://www.ibm.com/certify/exam/zse1f0-1000-154 |
IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Model training, evaluation, and optimization - Supervised and unsupervised learning concepts - Model deployment and monitoring |
| Topic 2: Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Deploying models as REST APIs - Watson Discovery overview - Monitoring deployed models |
| Topic 3: Data Visualization and Storytelling | 15-20% | - Visualization best practices - Communicating findings to stakeholders - Interactive dashboards and reports |
| Topic 4: Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components - Data collection, preparation, and exploration |
| Topic 5: Watson Studio and Watson Knowledge Catalog | 20-25% | - Data asset management - Project management and collaboration - Data governance and cataloging - AutoAI and automatic model building |
C1000-154 Exam FAQs for 2026 Candidates
Which certification does the C1000-154 exam lead to?
The C1000-154 exam is the official IBM exam behind the IBM Certification certification, validating the skills measured by the IBM Watson Data Scientist v1 credential. It sits at the Intermediate level of the IBM certification program. It also connects to IBM Data Science Professional Certificate, IBM Applied AI Professional Certificate, so the knowledge you build here carries over to those tracks as well.
How many questions are on the C1000-154 exam, and how much time do I get?
The C1000-154 exam contains 61 questions to be completed within 90 minutes. Before exam day, divide the available time by the question count to work out a comfortable per-question pace, and mark any item that eats into it so you can return later instead of getting stuck. Timed sessions in the Getcertkey test engines make that pacing automatic — run at least two full-length mock exams under the clock so time pressure never becomes the reason you drop points.
What score do I need to pass the C1000-154 exam, and what does it cost?
The passing score for the C1000-154 exam is 63%, and the official registration fee is $200 USD. Retakes are not discounted — every new attempt means paying the full fee again — so it pays to measure yourself before you book. Work through the 82 practice questions on Getcertkey, sit a timed practice test, and schedule your exam only when your scores are consistently comfortable. That simple habit is the cheapest exam strategy there is.
Are there any prerequisites for the C1000-154 exam?
Recommended: IBM Data Science Professional Certificate or equivalent experience with IBM Cloud and data science tools Requirements can change when IBM revises its certification program, so confirm the current eligibility rules on the official exam page before you register.
Can I try the C1000-154 practice questions before I buy?
Yes. Getcertkey provides a free C1000-154 PDF demo so you can review the question style and answer quality before purchasing. Every purchase also includes 365 days of free updates — if IBM revises the exam during that period, the updated material reaches you at no cost. Once the free-update year ends, you can extend your update service at a 50% discount.
What if I fail the C1000-154 exam, and how is my order delivered?
Every IBM Watson Data Scientist v1 purchase on Getcertkey is covered by a 100% money-back guarantee with clear conditions: if you take the corresponding exam within 60 days of your purchase and do not pass, you can claim a full refund by submitting a scanned copy of your exam enrollment slip and your official score report as a PDF within two days of the exam date; claims are processed within seven days of submission. The guarantee does not apply to exams taken within three days of purchase, to material that was downloaded but never used in an exam attempt, or to free products and expired orders, and the candidate name must match the payer name. If you would rather not take a refund, you can instead exchange your purchase for two free exam preparation products of equal value and keep the update service on your original product.
Delivery is instant: your download is sent to your email within one minute of payment, with no limit on how many computers you may install the material on. If nothing arrives within two hours, check your spam folder and contact customer service for help.
What topics are covered in the C1000-154 exam?
The IBM Watson Data Scientist v1 exam blueprint is organized into 5 domains. The first three are:
- Watson Studio and Watson Knowledge Catalog — 20-25% of the exam
- Data Science and Watson Fundamentals — 20-25% of the exam
- Data Visualization and Storytelling — 15-20% of the exam
For the complete domain-by-domain breakdown, scroll up to the full exam topics outline above and use it to plan how you distribute your study time.
IBM Watson Data Scientist v1 Sample Questions:
Question #1
Which of the following is a critical first step in understanding a business problem for data science projects?
A. Selecting the machine learning algorithm
B. Choosing the visualization tools
C. Deploying the model
D. Defining the project scope
Question #2
Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
A. Precision
B. Accuracy
C. F1-score
D. Recall
Question #3
Which hyperparameter is NOT commonly adjusted in a deep learning model?
A. Learning rate
B. The color of the model's output
C. Activation function
D. Number of layers
Question #4
Which method is NOT traditionally used for collecting data?
A. Using Python APIs for external data
B. Scraping data from a webpage
C. Predicting future trends with machine learning models
D. Using SQL to fetch data from a data warehouse
Question #5
Which analytic technique is NOT typically used to address business requirements?
A. Regression analysis
B. Proofreading
C. Clustering
D. Decision trees
Solutions:
| Question #1 Correct Answer: D | Question #2 Correct Answer: C | Question #3 Correct Answer: B | Question #4 Correct Answer: C | Question #5 Correct Answer: B |
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