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C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist Free Practice Exam Questions (2025 Updated)

Prepare effectively for your IBM C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist certification with our extensive collection of free, high-quality practice questions. Each question is designed to mirror the actual exam format and objectives, complete with comprehensive answers and detailed explanations. Our materials are regularly updated for 2025, ensuring you have the most current resources to build confidence and succeed on your first attempt.

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Total 62 questions

What are three operators used by genetic programming? (Choose three.)

A.

reciprocation

B.

mutation

C.

duel

D.

selection

E.

sheltering

F.

crossover

What are two key characteristics of cloud architecture that could benefit AI applications? (Choose two.)

A.

constant attention needed for maintenance and support of the cloud platform

B.

capable of managing and handling dynamic workloads with automatic recovery from failures

C.

hybrid clouds enable the deployment of distributed large neural networks

D.

support for common business oriented language (COBOL) applications

E.

the hardware requirement can be scaled up as per the demand

Which fine-tuning technique does not optimize the hyperparameters of a machine learning model?

A.

grid search

B.

population based training

C.

random search

D.

hyperband

What are three elements that are typically part of a machine learning pipeline in scikit-learn or pyspark? (Choose three.)

A.

model building

B.

data preprocessing

C.

model prediction

D.

business understanding

E.

use case selection

F.

data exploration

Which is a preferred approach for simplifying the data transformation steps in machine learning model management and maintenance?

A.

Implement data transformation, feature extraction, feature engineering, and imputation algorithms in one single pipeline.

B.

Do not apply any data transformation or feature extraction or feature engineering steps.

C.

Leverage only deep learning algorithms.

D.

Apply a limited number of data transformation steps from a pre-defined catalog of possible operations independent of the machine learning use case.

What is the first step in creating a custom model in Watson Visual Recognition service?

A.

Test the newly trained model.

B.

Document the errors from the built in models.

C.

Obtain image files containing objects to be classified and organize them into classes.

D.

Use IBM SPSS to create new machine learning classifiers.

The formula for recall is given by (True Positives) / (True Positives + False Negatives). What is the recall for this example?

A.

0.2

B.

0.25

C.

0.5

D.

0.33

What are two methods used to detect outliers in structured data? (Choose two.)

A.

multi-label classification

B.

isolation forest

C.

gradient descent

D.

one class Support Vector Machine (SVM)

E.

Word2Vec

What is used to scale large positive values during data cleaning?

A.

division by random numbers

B.

square

C.

logarithm

D.

subtract median

Which IBM Watson Machine Learning deployment method offers the ultimate flexibility in deploying a machine learning model?

A.

Watson Machine Learning Python client

B.

Watson Machine Learning FORTRAN client

C.

Watson Studio Project

D.

Watson Machine Learning REST API

A classification task has examples that are labeled as belonging to one of two classes:

•90% of the examples belong to class-1

•10% belong to class-2

Which two techniques are appropriate to deal with the class imbalance? (Choose two.)

A.

apply dimensionality reduction to the features before training

B.

impose an additional cost on the model for making classification mistakes on the minority class during training

C.

lower the detection threshold of the minority class after training

D.

oversample the minority class and/or undersample the majority class

E.

after training, divide the model accuracy of each class by the proportion that they represent in the dataset

Considering one ML application is deployed using Kubernetes, its output depends on the data which is constantly stored in the model, if needing to scale the system based on available CPUs, what feature should be enabled?

A.

persistent storage

B.

vertical pod autoscaling

C.

horizontal pod autoscaling

D.

node self-registration mode

Given the following sentence:

The dog jumps over a fence.

What would a vectorized version after common English stopword removal look like?

A.

['dog', 'fence', 'run']

B.

['fence', 'jumps']

C.

['dog', 'fence', 'jumps']

D.

['a', 'dog', 'fence', 'jumps', 'over', 'the']

What is the meaning of "deep" in deep learning?

A.

To go deep into the loss function landscape.

B.

The higher the number of machine learning algorithms that can be applied, the deeper is the learning.

C.

A kind of deeper understanding achieved by any approach taken.

D.

It indicates the many layers contributing to a model of the data.

Which is the most important thing to ensure while collecting data?

A.

samples collected are skewed with each other

B.

samples collected are all strongly correlated with each other

C.

samples collected adequately cover the space of all possible scenarios

D.

samples collected focus only on the most common cases

Which situation would disqualify a machine learning system from being used for a particular use case?

A.

The use case requires a 100% likelihood of making a correct/true prediction.

B.

Training and testing data for the model contain outliers.

C.

Data for the machine learning model is available only as static CSV files.

D.

The neural network for the model requires significantly more computing power than a logistic regression model.

A neural network is trained for a classification task. During training, you monitor the loss function for the train dataset and the validation dataset, along with the accuracy for the validation dataset. The goal is to get an accuracy of 95%.

From the graph, what modification would be appropriate to improve the performance of the model?

A.

increase the depth of the neural network

B.

insert a dropout layer in the neural network architecture

C.

increase the proportion of the train dataset by moving examples from the validation dataset to the train dataset

D.

restart the training with a higher learning rate

Which algorithm is best suited if a client needs full explainability of the machine learning model?

A.

decision tree

B.

logistic regression

C.

support vector machine (SVM)

D.

recurrent neural network

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Total 62 questions
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