iPass4sure.net

Certification Practice Test | PDF Questions | Actual Questions | Test Engine | Pass4Sure

C1000-059 : IBM AI Enterprise Workflow V1 Data Science Specialist Exam

IBM C1000-059 Questions & Answers

Full Version: 136 Q&A


Latest C1000-059 Exam Questions and Practice Tests 2025 - Killexams.com


C1000-059 Dumps

C1000-059 Braindumps C1000-059 Real Questions C1000-059 Practice Test C1000-059 Actual Questions


killexams.com


IBM


C1000-059


IBM AI Enterprise Workflow V1 Data Science Specialist


https://killexams.com/pass4sure/exam-detail/C1000-059

Question: 1


Which of the following is a popular framework used for distributed data processing and machine learning tasks in the IBM AI Enterprise Workflow?


  1. TensorFlow

  2. PyTorch


    cikit-learn wer: C

    anation: The popular framework used for distributed data processing a hine learning tasks in the IBM AI Enterprise Workflow is Apache Spa che Spark provides a unified analytics engine for big data processing a orts various programming languages, including Python, Scala, and Ja

    distributed computing capabilities, allowing efficient processing of l datasets and enabling scalable machine learning workflows.


    stion: 2


    ch of the following is an unsupervised learning algorithm used for nsionality reduction?


    means clustering ecision tree

    Apache Spark

  3. S


Ans


Expl nd

mac rk.

Apa nd

supp va. It

offers arge-

scale


Que


Whi dime


  1. K-

  2. D

  3. Support Vector Machine (SVM)

  4. Principal Component Analysis (PCA) Answer: D

Explanation: Principal Component Analysis (PCA) is an unsupervised learning algorithm commonly used for dimensionality reduction. PCA transforms a

high-dimensional dataset into a lower-dimensional space while preserving the most important patterns or variations in the data. It achieves this by identifying the principal components, which are linear combinations of the original features that capture the maximum variance in the data. By reducing the dimensionality of the data, PCA can simplify complex datasets, remove noise, and improve computational efficiency in subsequent analyses.



ch of the following techniques is used to address the problem of overfi achine learning models?


egularization eature scaling ross-validation nsemble learning


wer: A


anation: Regularization is a technique used to address the problem of fitting in machine learning models. Overfitting occurs when a model mes too complex and starts to capture noise or random fluctuations in ing data, leading to poor generalization to unseen data. Regularization duces a penalty term to the model's objective function, discouraging o plex or extreme parameter values. This helps to control the model's plexity and prevent overfitting by finding a balance between fitting th

Question: 3


Whi tting

in m


  1. R

  2. F

  3. C

  4. E

Ans Expl

over

beco the

train

intro verly

com

com e

training data well and generalizing to new data.


Question: 4


In the context of data science, what does the term "feature engineering" refer to?

  1. Creating artificial intelligence models

  2. Extracting relevant features from raw data

  3. Developing data visualization techniques

  4. Implementing data cleaning algorithms Answer: B

cting relevant features from raw data. It involves transforming the ra format that is suitable for machine learning algorithms to process an

yze. Feature engineering includes tasks such as selecting important bles, combining or transforming features, handling missing data, and ding categorical variables. The goal of feature engineering is to enhan redictive power of machine learning models by providing them with ningful and informative input features.


stion: 5


is the technique called for vectorizing text data which matches the w fferent sentences to determine if the sentences are similar?


up of Vectors ox of Lexicon ack of Sentences ag of Words

Explanation: In data science, "feature engineering" refers to the process of

extra w data

into a d

anal varia

enco ce

the p mea


Que


What ords

in di


  1. C

  2. B

  3. S

  4. B


Answer: D


Explanation: The correct technique for vectorizing text data to determine sentence similarity is called "Bag of Words" (BoW). In this technique, the text data is represented as a collection or "bag" of individual words, disregarding grammar and word order. Each word is assigned a numerical value, and the presence or absence of words in a sentence is used to create a vector

representation. By comparing the vectors of different sentences, similarity or dissimilarity between them can be measured.


Question: 6



andom Forest

nearest neighbors (KNN)

ong Short-Term Memory (LSTM) daBoost


wer: C


anation: Long Short-Term Memory (LSTM) is a popular algorithm us atural language processing (NLP) tasks, particularly text classification ment analysis. LSTM is a type of recurrent neural network (RNN) tha tively capture long-range dependencies in sequential data, such as nces or documents. It is well-suited for handling and analyzing text d o its ability to model context and sequential relationships. LSTM has ly applied in various NLP applications, including machine translation, ch recognition, and text generation.


stion: 7

Which of the following is a popular algorithm used for natural language processing tasks, such as text classification and sentiment analysis?


  1. R

  2. K-

  3. L

  4. A


Ans


Expl ed

for n and

senti t can

effec

sente ata

due t been

wide spee


Que


Which of the following evaluation metrics is commonly used for classification tasks to measure the performance of a machine learning model?


  1. Mean Squared Error (MSE)

  2. R-squared (R^2)

  3. Precision and Recall

Answer: C


ive instances. These metrics are particularly useful when dealing with lanced datasets or when the cost of false positives and false negatives rent. They provide insights into the model's ability to make accurate ive predictions and identify relevant instances from the dataset.


stion: 8

ch of the following is an example of a supervised learning algorithm? means clustering

priori algorithm inear regression

rincipal Component Analysis (PCA) wer: C

anation: Linear regression is an example of a supervised learning ithm. In supervised learning, the algorithm learns from labeled trainin

Explanation: Precision and Recall are commonly used evaluation metrics for classification tasks. Precision measures the proportion of correctly predicted positive instances out of all instances predicted as positive, while Recall measures the proportion of correctly predicted positive instances out of all true posit

imba is

diffe posit


Que


Whi


  1. K-

  2. A

  3. L

  4. P

Ans Expl

algor g

data, where each instance is associated with a corresponding target or output value. In the case of linear regression, the algorithm aims to find the best-fitting linear relationship between the input features and the continuous target variable. It learns a set of coefficients that minimize the difference between the predicted values and the actual target values. Once trained, the linear regression model can be used to make predictions on new, unseen data.

Which of the following is the goal of the backpropagation algorithm in neural networks?


  1. to randomize the trajectory of the neural network parameters during training

    scale the gradient descent step in proportion to the gradient magnitud compute the gradient of the loss function with respect to the neural ork parameters


    wer: B


    anation: The goal of the backpropagation algorithm is to smoothly agate the gradient of the loss function back through the neural networ to update the network's parameters during training. By smoothing th ent, it helps to avoid getting trapped in small local minima and allows mization process to converge towards a better global minimum. domizing the trajectory, scaling the gradient descent step, or computin ent alone are not the specific goals of the backpropagation algorithm.


    stion: 10


    ch of the following techniques is commonly used for imputing missin

    to smooth the gradient of the loss function in order to avoid getting trapped in small local minima

  2. to e

  3. to netw

Ans Expl

prop k in

order e

gradi the

opti

Ran g the

gradi


Que


Whi g

values in a dataset?


  1. Random sampling

  2. Median imputation

  3. One-hot encoding

  4. Principal Component Analysis (PCA)

erved to a certain extent. However, it is important to note that imputati niques should be chosen carefully, considering the nature of the data a ntial impact on downstream analyses.

Explanation: Median imputation is a commonly used technique for imputing missing values in a dataset. In this approach, the missing values are replaced with the median value of the corresponding feature. Median imputation is particularly useful for handling missing values in numerical variables, as it preserves the central tendency of the data. Byfilling in missing values with the median, the overall distribution and statistical properties of the variable are pres on

tech nd

pote


User: Wesley*****

Thanks to Killexams.com, I was able to pass my C1000-059 exam with a score of 92%. The notes and questions and answers provided were extremely helpful, and they made the entire process smooth and easy for me. By reviewing the course notes and practicing with the exam simulator, I was well-prepared for the exam. The trainer communication and presentation skills topics were particularly well-done. I am grateful for Killexams.com excellent work.
User: Norma*****

Preparing for the C1000-059 exam was my New Years resolution this year, and I was anxious about the process. Thanks to killexams.com, I was able to study, prepare, and pass the exam with ease. Their package included every query I received on the C1000-059 exam, making the experience stress-free and rewarding. I believe that the killexams.com experience is worth the investment.
User: Shasha*****

Thanks to killexams.com, I was able to pass the C1000-059 exam with a strong score. The exam was tough, but their questions were easy to memorize and accurate. I credit killexams.com for providing me with the knowledge I needed to pass the exam.
User: Nata*****

With only 12 days left to prepare for the C1000-059 exam, I felt overwhelmed and in need of a quick and effective guide. Fortunately, I found Killexams and their Questions and Answers. Their concise answers were easy to complete within 15 days, and I was able to score 88% in the actual C1000-059 exam. The practice tests they supplied were also very helpful, with 90% of the questions appearing on the actual exam. I owe my success to Killexams.
User: Gregory*****

I am thrilled to have passed my C1000-059 exam with a nearly perfect score of 98% thanks to Killexams! The material in the package is accurate and valid, just like the questions on my actual exam. I was able to answer most of the questions easily, and some of the extra questions were very similar to the ones covered in the test guide, so I had no trouble answering them. This not only helped me expand my professional knowledge, but it also gave me a smooth path to my C1000-059 certification.

Features of iPass4sure C1000-059 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 136 Q&A

Get Full Version

All IBM Exams

IBM Exams

Certification and Entry Test Exams

Complete exam list