C1000-154 Exam Dumps – Reliable Way to Pass and Get Certified

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1. A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions).

Which metric is a good indicator of the performance of the virtual assistant?

The Area Under the Curve (AUC)

Measure escalated calls using A/B testing

The Root Mean Squared Error (RMSE) of words

The F1 score of predicted intents in the Analytics tab

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2. Key metrics for a solution should be defined based on:

The personal preferences of the project stakeholders

The most recent technological trends

The specific objectives and desired outcomes of the project

The number of available data scientists

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3. What is a benefit of creating data pipelines to automate the model lifecycle?

It necessitates frequent manual updates and checks

Reduces the need for understanding the underlying data

It provides a structured approach to processing, validating, and deploying models

Encourages a one-size-fits-all approach to model development

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4. An essential aspect of the ETL (Extract, Transform, Load) process is:

Extracting the least amount of data for simplicity

Transforming data exclusively in cloud environments

Loading data into a single, centralized database for analysis

Ensuring data quality and consistency throughout the process

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5. 1.When anticipating additional data sources that might be relevant, what is a crucial factor to consider?

The color scheme of the data visualization

The data source's popularity on social media

The relevance of the data source to the business problem

The graphical interface of the data source

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6. In Cognos Analytics, which two features distinguish stories from dashboards?

Stories convey a conclusion.

Stories provide a narrative over time.

Stories can be embedded in websites or documents.

Stories are text-based and do not contain visualizations.

Stories automatically load different filters for different users.

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7. Which method is NOT traditionally used for collecting data?

Using SQL to fetch data from a data warehouse

Scraping data from a webpage

Predicting future trends with machine learning models

Using Python APIs for external data

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8. In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?

It focuses on maintaining and operating solutions at scale.

It involves the rapid building of prototypes to validate ideas.

It emphasizes understanding user outcomes and business needs.

It is primarily concerned with the technical deployment of solutions.

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9. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?

TensorFlow

PyTorch

Pandas

Keras

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10. In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?

It ensures that the model is trained on the maximum amount of data possible

It allows for the model to be validated and tested on different subsets of data to check its generalization ability

It guarantees that the model will perform with 100% accuracy on unseen data

It increases the computational complexity without improving model performance

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11. Which of the following is NOT a direct benefit of connecting to data sources using Cloud Pak for Data?

Streamlining the integration of diverse data sources

Automatically generating insights without data analysis

Facilitating secure and scalable data connectivity

Enhancing collaboration across data science and IT teams

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12. In classification models, which of the following metrics is NOT directly derived from the confusion matrix?

Precision

Recall

Mean Absolute Error (MAE)

F1-score

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13. Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?

Grid Search

Random Search

Sequential Search

Binary Search

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14. What is the primary purpose of hyperparameter tuning in machine learning models?

To reduce the training time of the model to an absolute minimum

To adjust the model's complexity to improve its performance on unseen data

To increase the number of features in the dataset automatically

To ensure the model uses all available computational resources

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15. Which type of machine learning algorithm would be most appropriate for predicting house prices based on various features like location, size, and number of bedrooms?

Clustering

Regression

Classification

Dimensionality Reduction

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16. Choosing the best model often involves trade-offs.

Which scenario represents such a trade-off?

Preferring a model with higher accuracy over one that is slightly less accurate but much more interpretable

Choosing the model with the largest number of features, regardless of performance

Opting for the most complex model to ensure ease of use

Selecting a model based solely on its execution speed, without regard to accuracy

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17. What does the term "complexity" in model comparison refer to?

The aesthetic appeal of the model's graphical representations

The number of hyperparameters that need to be tuned

The amount of computational resources required for training and inference

The size of the dataset the model can handle

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18. In the context of building models, why is it important to select a tool based on algorithm requirements and expertise?

Tools with the most features should always be selected to ensure model complexity.

Selecting a tool that matches the team's expertise ensures more efficient model development and troubleshooting.

It is legally required to use only certain tools for specific types of data.

All machine learning tools are essentially the same, making the selection process trivial.

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19. Given the Confusion matrix below, which is the formula for specificity?

TN/(TN + FP)

TP/(FP + TP)

TP/(FN + TP)

(TP + TN)/(FN + FP + TN + TP)

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20. How do you determine which tool to use based on algorithm requirements and expertise?

Always use the most complex tool to ensure the model's accuracy.

Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.

Consider the tool's compatibility with the algorithm requirements and the team's expertise.

Choose the newest tools on the market for the most up-to-date features.

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21. In the context of model selection, explainability refers to:

How colorful and visually appealing the model's output is.

The model's ability to operate without any data.

The ease with which humans can understand how the model makes decisions.

The complexity of the algorithm used to build the model.

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22. Automating data processing and model deployment with jobs in Watson Studio helps to:

Increase the need for manual intervention in the model lifecycle

Reduce the scalability of deployed solutions

Enhance the reproducibility and efficiency of model deployments

Limit the ability to update models based on new data

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23. To add data assets from the catalog to a project in Cloud Pak for Data, which step is essential?

Selecting random data sets for variety

Maximizing the volume of data regardless of relevance

Assessing the compatibility of data formats

Browsing data assets based solely on their names

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24. The process of aligning on user intents for a solution involves:

Determining the technical feasibility exclusively

Understanding the business model in depth

Identifying and understanding the needs and goals of end-users

Focusing on the data management strategies

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