Learn with IBM : C1000-154 training material for 100% pass
Updated: Jul 24, 2026
No. of Questions: 82 Questions & Answers with Testing Engine
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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 |
| Real Exam Qty: | 61 |
| Passing Score: | 63% |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Multiple Response |
| Related Certifications: | IBM Data Science Professional Certificate IBM Applied AI Professional Certificate |
| Sample Questions: | IBM C1000-154 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: 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 2: Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Monitoring deployed models - Watson Assistant integration - Watson Discovery overview |
| Topic 3: Data Visualization and Storytelling | 15-20% | - Visualization best practices - Communicating findings to stakeholders - Interactive dashboards and reports |
| Topic 4: Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - Data asset management - AutoAI and automatic model building - Data governance and cataloging |
| Topic 5: 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 |
IBM Watson Data Scientist v1 Sample Questions:
1. When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
A) Choosing algorithms that are only based on supervised learning.
B) The algorithm that requires the least amount of data preprocessing.
C) The popularity of the algorithm in recent academic papers.
D) Compatibility of the algorithm with the data characteristics and the predictive task.
2. When helping businesses articulate and define problems, what is an essential first step?
A) Selecting the analytical techniques
B) Defining key performance indicators (KPIs)
C) Identifying potential data sources
D) Establishing a clear problem statement
3. The first step in performing exploratory data analysis (EDA) typically involves:
A) Determining the hypothesis for the analysis
B) Connecting to as many data sources as possible
C) Selecting a random sample of data to analyze
D) Choosing a color palette for data visualization
4. Key metrics for a solution should be defined based on:
A) The most recent technological trends
B) The specific objectives and desired outcomes of the project
C) The personal preferences of the project stakeholders
D) The number of available data scientists
5. The ROC curve is a graphical representation that shows the performance of a classification model at all classification thresholds.
What does ROC stand for?
A) Recall Operation Curve
B) Receiver Operating Characteristic
C) Random Output Curve
D) Regression Operation Characteristic
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: B |
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