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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 Excellerate 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

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