IBM Watson Specialist Data Scientist (C1000-154) Certification Sample Questions

The purpose of this Sample Question Set is to provide you with information about the IBM Watson Data Scientist v1 exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the C1000-154 certification test. To get familiar with real exam environment, we suggest you try our Sample IBM Watson Specialist Data Scientist Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual IBM Certified Data Scientist - Watson Specialist v1 certification exam.

These sample questions are simple and basic questions that represent likeness to the real IBM C1000-154 exam questions. To assess your readiness and performance with real time scenario based questions, we suggest you prepare with our Premium IBM Watson Specialist Data Scientist Certification Practice Exam. When you solve real time scenario based questions practically, you come across many difficulties that give you an opportunity to improve.

IBM C1000-154 Sample Questions:

01. 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

 

02. 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

 

03. 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

 

04. 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.

 

05. 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.

 

06. 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

 

07. 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

 

08. 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)

 

09. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?

a) vim

b) pip

c) sudo

d) bash

e) conda

 

10. Given the Confusion matrix below, which is the formula for specificity?

a) TN/(TN + FP)

b) TP/(FP + TP)

c) TP/(FN + TP)

d) (TP + TN)/(FN + FP + TN + TP)

Answers:

Note: For any error in IBM Certified Data Scientist - Watson Specialist v1 (C1000-154) certification exam sample questions, please update us by writing an email on feedback@edusum.com.