IBM C1000-144 Exam will be Replaced by C1000-177 Foundations of Data Science using IBM watsonx

dumps2024-10-25T11:00:05+08:00

IBM C1000-144 exam will be retired at the end of October and will be replaced by C1000-177 Foundations of Data Science using IBM watsonx. If you are preparing for the IBM C1000-144 exam, please take your time to pass it. There are 61 questions in the real IBM Certification C1000-144 exam, and you have 90 minutes to complete the test. The number of questions required to pass is 45. The available language is English and the price is $200. We provide the latest IBM C1000-144 exam dumps, which can make sure you pass easily. Free IBM Certification C1000-144 exam dumps are available below.

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1. Why is it important to create hypotheses about the behavior of the AI system?

It simplifies the coding process for developers

It helps predict and mitigate potential risks associated with the system

It is primarily for marketing purposes

It fulfills a legal requirement for AI development

2. In the context of machine learning, what does the term 'model drift' refer to?

The migration of the model from one server to another

The change in model parameters due to new updates

The change in model performance due to changes in underlying data patterns

The physical movement of the hardware running the model

3. Which approach is recommended for prioritizing business opportunities when planning an MVP?

Choosing the most straightforward implementation irrespective of impact

Assessing the potential return on investment and strategic fit

Prioritizing based on the preference of the project manager

Focusing solely on technological innovation

4. What is the primary purpose of monitoring a model in production?

To enhance the visual appeal of the model's output

To ensure the model's performance remains stable over time

To reduce the model's complexity for easier understanding

To increase the model's training speed

5. Which of the following are essential tasks when preparing data for exploratory analysis? (Choose Three)

Labeling data accurately

Ensuring data is representative of the entire population

Assigning random values to missing data points

Anonymizing sensitive information

Organizing data chronologically

6. In the context of classification, what does the term 'overfitting' refer to?

The model performs equally well on the training and test datasets

The model performs poorly on both training and test datasets

The model performs too well on the training dataset but poorly on unseen data

The model requires too much time to train due to large dataset

7. Which of the following are considered direct effects of an AI solution? (Choose Two)

Enhancements in process efficiency for which the AI was designed

Increased job satisfaction among employees not using the AI directly

Reduction in operational costs due to automation

New market opportunities stemming from the innovation

8. How do you assess the feasibility of an AI solution?

By evaluating the available technology and resources

By creating detailed financial models only

By ensuring the project is the top priority of the organization

By hiring external consultants to validate the solution

9. What is the primary use of the WHERE clause in an SQL query?

To specify which columns to retrieve

To limit the data that fits certain conditions

To identify the tables involved in the query

To denote the end of the SQL query

10. What is the first step in aligning on user intents for an AI solution?

Prototyping the solution

Identifying key stakeholders

Conducting a market analysis

Documenting technical requirements

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11. How does IBM Garage Methodology suggest measuring success for an MVP?

By the number of features implemented

Through stakeholder satisfaction and feedback

By comparing the MVP to competitor products

Solely by financial metrics achieved

12. In assessing progress on the AI Ladder, which aspects should be considered? (Choose Two)

The quality and accessibility of data

The color palette of the user interface

Integration capabilities with existing systems

Branding and marketing strategies

13. When monitoring models in production, what aspect is crucial for maintaining long-term reliability?

Regularly updating the user interface

Ensuring the model is scalable to handle increased loads

Reducing the number of inputs to the model

Focusing solely on increasing model speed

14. Which feature engineering technique can be used to simplify models and improve interpretability?

One-hot encoding categorical variables

Normalizing continuous variables

Removing correlated features

Increasing the number of features

15. How does feature scaling benefit the process of exploratory data analysis?

It changes the underlying data distribution

It makes different variables comparable

It simplifies the database management system

It eliminates the need for data cleaning

16. For implementing dimensional reduction, which method would be most effective when dealing with highly nonlinear data?

Linear Discriminant Analysis (LDA)

PCA

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Factor Analysis

17. What are two reasons a data point would be treated as an outlier?

If the value is greater than mean

If the value is greater than median

If the value is greater than standard deviation

If the value is below the upper end of the bottom quartile by more then 1.5 times the interquartile range

If the value is above the lower end of the top quartile by more then 1.5 times the interquartile range

18. Which practice is least effective in configuring environments for training machine learning models?

Using virtual environments to manage dependencies

Using the latest but unstable software versions

Regularly updating libraries to their stable versions

Allocating resources based on model requirements

19. Why is logistic regression considered a linear classifier?

Because it is only capable of linear regression tasks

Because it uses a linear decision boundary to separate classes

Because it applies a nonlinear transformation to the input features

Because it computes the decision boundary using a non-linear optimization

20. What considerations should be made when evaluating the ethical implications of a business problem? (Choose Three)

Potential for AI to replace human jobs

Environmental impact of AI solutions

Impact on company profit margins

Consequences for user privacy and autonomy

Speed of implementation

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21. Which of the following is a common use case for recommendation engines?

Predicting property prices

Detecting fraudulent credit card transactions

Suggesting products to customers based on past purchases

Categorizing news articles into topics

22. You need to compare sales performance across different regions.

Which type of chart would most effectively serve this purpose?

Histogram

Box plot

Bar chart

Heatmap

23. If the goal is to explore the central tendency and variability of a dataset, which types of plots would be most informative?

Bar chart and line plot

Histogram and box plot

Scatterplot and heatmap

Pie chart and line plot

24. Which approach is best for refining an AI solution based on feasibility assessment?

Increasing the complexity of the solution

Reducing scope to match available resources and capabilities

Outsourcing the entire project

Ignoring feasibility concerns to speed up deployment

25. In SQL, how would you extract the 'name' and 'age' columns from a table named 'customers'?

SELECT name, age FROM customers;

EXTRACT name, age FROM customers;

GET name, age IN customers;

PULL name, age OUT OF customers;

26. Which algorithm is most appropriate for non-linear classification problems?

Linear regression

Logistic regression

Support Vector Machine with non-linear kernels

K-means clustering

27. Which approach would not be suitable for assessing model fairness?

Analyzing confusion matrices for different subgroups

Using the same performance metric for all models

Conducting audits on model decisions

Implementing external fairness monitoring tools

28. Which techniques ensure a model can explain its decisions and predictions?

Implementing deep learning models exclusively

Using highly non-linear models without any simplification

Integrating explanation frameworks like LIME or SHAP

Minimizing the use of regularization techniques

29. Converting a neural network into the newest version of TensorFlow or another deep-learning package is what type of performance drift or software decay?

Data changes

Concept drift

Software changes

Sampling bias and selection bias changes

30. How can ensemble modeling improve machine learning performance?

By simplifying the models to reduce computation time

By combining multiple models to reduce variance and bias

By using a single, highly accurate model

By focusing exclusively on increasing model accuracy

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31. Which is a primary goal of AI design thinking in relation to business problems?

Maximizing the speed of development

Understanding user needs and pain points

Choosing the most advanced AI technologies

Ensuring the project is completed under budget

32. What is the primary goal when splitting data into training, testing, and validation sets?

To increase the computational speed of model training

To ensure the model generalizes well to new data

To use all available data for training to improve accuracy

To test all models on the same set of data

33. Which method in Pandas would you use to rename the columns of a DataFrame?

df.rename_columns()

df.columns = ['new_name1', 'new_name2']

df.rename({'old_name': 'new_name'}, axis=1)

df.set_names(['new_name1', 'new_name2'])

34. In K-Nearest Neighbors (KNN), what does K represent?

The number of clusters to form

The number of training samples to use

The number of features to consider

The number of nearest neighbors to consider

35. What is the benefit of feature scaling in model training?

It increases the number of features for better accuracy

It helps algorithms converge faster by normalizing feature magnitudes

It decreases the transparency of the model

It is only useful for unsupervised learning

36. Given an SQL table 'Books' with fields Title, Author, and Genre, which query would return a list of unique Genre values?

SELECT Genre FROM Books;

SELECT SET Genre FROM Books;

SELECT UNIQUE Genre FROM Books;

SELECT DISTINCT Genre FROM Books;

37. In the context of anomaly detection, what is the algorithm primarily searching for?

Patterns that do not conform to expected behavior

The best way to group similar data points

The optimal number of clusters in the data

The strongest predictors of a target variable

38. What are effective strategies for handling missing data? (Choose Two)

Deleting all rows with any missing values

Imputing missing values using statistical methods

Using a machine learning model to predict missing values

Ignoring missing data during analysis

IBM C1000-144 Exam will be Replaced by C1000-177 Foundations of Data Science using IBM watsonx

IBM C1000-144 Exam will be Replaced by C1000-177 Foundations of Data Science using IBM watsonx

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