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Exam C1000-154: IBM Watson Data Scientist v1

The IBM Watson Data Scientist is a person who is well versed with IBM Watson Tools. They focus on business outcomes and actionable insights to help shape business strategy.  They understand the application of the tools and technologies, the interaction between them, and where to apply them in the Data Science lifecycle.  They can perform exploratory data analysis that includes data preparation. They leverage formal data science methods to work with data and models in a governed and compliant manner.

Exam Information

Exam Code: C1000-154

Exam Name: IBM Watson Data Scientist v1

Number of questions: 60

Number of questions to pass: 41

Time allowed: 90 minutes

Languages: English

Price: $200 USD

Certification: IBM Certified Data Scientist - Watson Specialist v1

Exam Objectives

Section 1: Understand the business problem    7%

Section 2: Collect and explore the data    15%

Section 3: Prepare the data    18%

Section 4: Build the model   17%

Section 5: Evaluate the model   12%

Section 6: Deploy the solution   10%

Section 7: Governance and compliance   5%

Section 8: Vizualization and Storytelling    10%

Section 9: Strategy and Lifecycle   6%

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1. An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.

During which phase should these non-informative entries be removed in the CRISP-DM model?

A. Modeling

B. Data Preparation

C. Data Understanding

D. Business Understanding

Answer: B

2. Why is it important to create data splits that are reproducible?

A. To ensure that each model run can be exactly replicated for verification and comparison

B. To guarantee that the model will perform with 100% accuracy on unseen data

C. To use more data for testing than for training

D. To allow for larger test sets for more comprehensive testing

Answer: A

3. Which two graph types are used in EDA to show the relationship between two or more quantitative variables?

A. Heat map

B. Box plot

C. Histogram

D. Scatter plot

E. Stem-and-leaf plot

Answer: A, D

4. Which statement describes bagging?

A. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.

B. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.

C. Building models in parallel and aggregating their predictions to select the final prediction.

D. Building models and using their output as features into a final model.

Answer: C

5. In Cognos Analytics, which two features distinguish stories from dashboards?

A. Stories convey a conclusion.

B. Stories provide a narrative over time.

C. Stories can be embedded in websites or documents.

D. Stories are text-based and do not contain visualizations.

E. Stories automatically load different filters for different users.

Answer: A, B

6. Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?

A. Key

B. Order

C. Filter

D. Condition

Answer: D

7. A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions). Which metric is a good indicator of the performance of the virtual assistant?

A. The Area Under the Curve (AUC)

B. Measure escalated calls using A/B testing

C. The Root Mean Squared Error (RMSE) of words

D. The F1 score of predicted intents in the Analytics tab

Answer: B

8. Which statistical method reduces the number of attributes by lumping highly correlated attributes together?

A. Binning

B. Principal Component Analysis (PCA)

C. Long Short Term Memory Network (LSTM)

D. Synthetic Minority Over-sampling Technique (SMOTE)

Answer: B