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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, Japanese |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Multiple Response |
| Exam Duration: | 90 minutes |
| Passing Score: | 68% - 70% |
| Exam Price: | $200 USD |
| Related Certifications: | IBM Watson Studio IBM Cloud Pak for Data |
| Real Exam Qty: | 60 |
| Recommended Training: | IBM C1000-154 Study Guide IBM Watson Studio Learning Path |
| Exam Registration: | IBM Certification Portal Pearson VUE Registration |
| Sample Questions: | IBM C1000-154 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE authorized test centers |
| Pre Condition: | No formal prerequisites; recommended 6-12 months hands-on experience with IBM Watson Studio and data science workflows |
| Official Syllabus URL: | https://www.ibm.com/certify/exams/C1000-154 |
IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) |
| Topic 2: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 3: Build the Model | 20% | - Perform hyperparameter tuning - Compare and select best performing models - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Topic 4: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 5: Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| Topic 6: Deploy the Solution | 10% | - Monitor model performance post-deployment - Deploy models as APIs in Watson - Ensure scalability and reliability |
| Topic 7: Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio |
| Topic 8: Prepare the Data | 18% | - Feature engineering and selection - Use Watson tools for data preparation - Handle missing values and outliers - Clean, transform, and normalize datasets |
IBM Watson Data Scientist v1 Sample Questions:
Question 1
Why is it important to create data splits that are reproducible?
A. To allow for larger test sets for more comprehensive testing
B. To ensure that each model run can be exactly replicated for verification and comparison
C. To guarantee that the model will perform with 100% accuracy on unseen data
D. To use more data for testing than for training
Question 2
Which analytic technique is NOT typically used to address business requirements?
A. Regression analysis
B. Decision trees
C. Proofreading
D. Clustering
Question 3
In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
A. It involves the rapid building of prototypes to validate ideas.
B. It focuses on maintaining and operating solutions at scale.
C. It emphasizes understanding user outcomes and business needs.
D. It is primarily concerned with the technical deployment of solutions.
Question 4
Which metric is commonly used to evaluate the performance of a regression model?
A. Precision
B. Recall
C. Accuracy
D. Mean Squared Error (MSE)
Question 5
Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
A. Sequential Search
B. Grid Search
C. Random Search
D. Binary Search
Solutions:
| Question 1 Answer: B | Question 2 Answer: C | Question 3 Answer: C | Question 4 Answer: D | Question 5 Answer: B |
By Asa
