IBM C1000-144 Questions & Answers

Full Version: 124 Q&A

Latest C1000-144 Exam Questions and Practice Tests 2025 - Killexams.com

C1000-144 Dumps

C1000-144 Braindumps C1000-144 Real Questions C1000-144 Practice Test C1000-144 Actual Questions

IBM

C1000-144

IBM Machine Learning Data Scientist v1

https://killexams.com/pass4sure/exam-detail/C1000-144

Which of the following metrics is commonly used to monitor model performance in production?

Mean Absolute Error (MAE)
Precision-Recall curve

rea Under the ROC Curve (AUC-ROC) wer: D
anation: The Area Under the ROC Curve (AUC-ROC) is a commonly ic for monitoring model performance in production. It provides a mea model's ability to discriminate between positive and negative instan
ng it suitable for binary classification problems.

stion: 2

When refining a machine learning model, which of the following techniqu e used for regularization?

regularization (Lasso) radient boosting
ropout regularization nsemble learning
R-squared (R2) score
A

Que

L1
G
D
E

Explanation: L1 regularization, also known as Lasso regularization, adds a penalty term to the model's loss function to encourage sparsity in the feature weights. It helps in selecting the most relevant features and prevents overfitting.

When evaluating a business problem for machine learning implementation, which of the following ethical implications should be considered?

Privacy concerns and data protection
ocial biases and fairness in decision-making nvironmental sustainability and resource consumption

wer: A

anation: When evaluating a business problem for machine learning ementation, it is crucial to consider ethical implications. Privacy conc ata protection should be addressed to ensure that personal and sensiti mation is handled securely and in compliance with relevant regulatio

stion: 4

When monitoring models in production, which of the following techniques sed for detecting data drift?

rincipal Component Analysis (PCA) means clustering
atistical hypothesis testing
Market competition and intellectual property rights
S
E

Que

P
K-
St
Ensemble learning Answer: C

Explanation: Statistical hypothesis testing can be used to detect data drift by comparing the statistical properties of the new data with the reference data. It helps in identifying changes in the data distribution and triggers appropriate

actions for model adaptation or retraining.

Question: 5

What is an important consideration when monitoring machine learning models in production?

ontinuously evaluating model fairness and bias ebuilding the model periodically with new data educing the number of model performance metrics

anation: When monitoring machine learning models in production, it i ntial to continuously evaluate and mitigate any biases or unfairness in el's predictions. This helps in ensuring ethical and unbiased decision- ng.

stion: 6

ch of the following methods can be used for model explainability? artial dependence plots

ackpropagation algorithm upport Vector Machines (SVM)

Tracking model accuracy on the training data

Que

P
B
S
Random Forest feature importance Answer: A

Explanation: Partial dependence plots are a technique used for model explainability. They show how the model's predictions change as a particular feature varies while holding other features constant. By visualizing the

relationship between individual features and the predicted outcome, partial dependence plotsprovide insights into the model's behavior and help in understanding its decision-making process.

Question: 7

ecursive Feature Elimination (RFE) rid search for hyperparameter tuning means clustering for feature grouping ross-validation for model evaluation

anation: Recursive Feature Elimination (RFE) is a technique used for re selection, which recursively removes features and builds models us emaining features. It ranks the features based on their importance and ts the optimal subset of features for the model.

stion: 8

ch of the following activities is part of the model deployment process? raining the model on the entire dataset

To implement the proper model, which of the following techniques can be used for feature selection?

R
G
K-
C

Que

T
Evaluating the model's performance on a validation set
Applying the model to new, unseen data
Conducting exploratory data analysis Answer: C

Explanation: Model deployment involves applying the trained model to new,

unseen data for making predictions or generating insights. This step is crucial to assess the model's performance in real-world scenarios.

Question: 9

During exploratory data analysis, which of the following techniques can be used for data preparation?

eature scaling and normalization rincipal Component Analysis (PCA)

eature extraction and dimensionality reduction utlier detection and removal

anation: During exploratory data analysis, feature extraction and nsionality reduction techniques can be employed to identify meaningf res and reduce the dimensionality of the dataset. This helps in improv odel's performance and reducing computational complexity.

User: Michael*****

I recently passed the c1000-144 exam, and I could not have done it without the assistance of Killexams.com. A few months ago, I failed the exam when I took it for the first time. However, the questions from Killexams.com were very similar to the actual ones, and I passed the exam with ease this time. I am grateful to the team at Killexams.com for their support.

User: Lydie*****

Balancing a demanding work schedule made studying for the c1000-144 exam challenging, but killexams.com’s testprep query and answer service was a revelation. Their clear explanations resolved all my doubts, leading to an impressive 96% score. I credit their outstanding resources for my success and highly recommend them to anyone preparing for this exam.

I have been using Killexams.com for a while on all my tests, and last week I passed the C1000-144 exam with outstanding marks. The material passed all my doubts and troubles, and I appreciate the stable and reliable material they provide.

User: Laika*****

I have recommended Killexams.com to several colleges and colleagues, and they have all been extremely satisfied. I want to thank Killexams.com for boosting my career and helping me plan for my exams. I have to say that I am your biggest fan because I passed my c1000-144 exam with the help of the test prep I purchased from you. I answered 86 out of 95 questions in the exam. You are an excellent training provider.

User: Ibrahim*****

Invaluable testprep training made my c1000-144 exam feel like a breeze, leading to an easy pass. Their magical resources simplified complex topics, and I am grateful for their exceptional platform.

Features of iPass4sure C1000-144 Exam

Files: PDF / Test Engine
Premium Access
Online Test Engine
Instant download Access
Comprehensive Q&A
Success Rate
Real Questions
Updated Regularly
Portable Files
Unlimited Download
100% Secured
Confidentiality: 100%
Success Guarantee: 100%
Any Hidden Cost: $0.00
Auto Recharge: No
Updates Intimation: by Email
Technical Support: Free
PDF Compatibility: Windows, Android, iOS, Linux
Test Engine Compatibility: Mac / Windows / Android / iOS / Linux

Premium PDF with 124 Q&A

Get Full Version

All IBM Exams

IBM Exams

Certification and Entry Test Exams

Complete exam list