C1000-154 Practice Test Questions Answers Updated 82 Questions
C1000-154 dumps & IBM Certification Sure Practice with 82 Questions
IBM Watson Data Scientist v1 Exam is intended for individuals who have a background in data science, machine learning, and artificial intelligence. C1000-154 exam covers a wide range of topics, including data preparation, data modeling, data visualization, machine learning, and deep learning. C1000-154 exam also tests the candidate's ability to apply these concepts to real-world scenarios and solve complex problems.
IBM C1000-154 exam is a part of IBM’s certification program, where prospective Watson data scientists must pass C1000-154 exam to get the certification. Passing the exam is crucial, as it is the reflection of your capabilities as a data scientist in using the Watson toolset, analyzing data, and deploying AI solutions. This credential will give data scientists an edge in the job market, as it signifies their mastery of one of the most advanced data science solutions.
NEW QUESTION # 28
Selecting the right model for a data science project depends on:
- A. The project's budget only
- B. The size of the dataset only
- C. The preference of the data scientist
- D. The type of data and the problem to be solved
Answer: D
NEW QUESTION # 29
What is a key disadvantage of using Grid Search for hyperparameter tuning?
- A. It can be computationally expensive and time-consuming due to its exhaustive nature
- B. It requires no prior knowledge of the hyperparameters
- C. It is unable to handle discrete parameters
- D. It is too quick and may miss out on evaluating some hyperparameters
Answer: A
NEW QUESTION # 30
Which of the following best exemplifies the use of the CRISP-DM methodology in a business context?
- A. A company following a strict top-down approach for all decisions
- B. A business starting with data collection before understanding the problem
- C. A team iterating between different stages as needed based on project feedback
- D. A project manager focusing exclusively on deployment
Answer: C
NEW QUESTION # 31
What is the key difference between batch processing and streaming in data processing?
- A. Streaming is suitable for large, historical datasets, whereas batch processing is for real-time data analysis
- B. Batch processing involves real-time data processing, whereas streaming does not process data
- C. Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives
- D. Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives
Answer: D
NEW QUESTION # 32
In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
- A. Extending the timeline of the project indefinitely
- B. Testing hypotheses with the smallest investment of time and resources
- C. Waiting for all possible features to be developed before release
- D. Maximizing the budget before the product launch
Answer: B
NEW QUESTION # 33
In the context of model selection, explainability refers to:
- A. The model's ability to operate without any data.
- B. The complexity of the algorithm used to build the model.
- C. The ease with which humans can understand how the model makes decisions.
- D. How colorful and visually appealing the model's output is.
Answer: C
NEW QUESTION # 34
Which of the following is true regarding cross-validation?
- A. It should be avoided as it leads to overfitting.
- B. It involves training the model on the entire dataset at once.
- C. It helps in identifying the model's performance variability across different data splits.
- D. It decreases the variability of the model performance estimation.
Answer: C,D
NEW QUESTION # 35
In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?
- A. Splitting based on the order of data collection
- B. Stratified split
- C. Random split without considering the target variable
- D. Using only the majority class for splitting
Answer: B
NEW QUESTION # 36
In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
- A. Focusing exclusively on the cost of storage
- B. Choosing the most aesthetically pleasing user interface
- C. Selecting the programming language with the least number of keywords
- D. Determining the computational power and memory requirements for the deployed solution
Answer: D
NEW QUESTION # 37
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?
- A. Classification
- B. Dimensionality Reduction
- C. Clustering
- D. Regression
Answer: D
NEW QUESTION # 38
An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?
- A. Modeling
- B. Data Preparation
- C. Business Understanding
- D. Data Understanding
Answer: B
NEW QUESTION # 39
When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
- A. Choosing algorithms that are only based on supervised learning.
- B. The popularity of the algorithm in recent academic papers.
- C. The algorithm that requires the least amount of data preprocessing.
- D. Compatibility of the algorithm with the data characteristics and the predictive task.
Answer: D
NEW QUESTION # 40
Which two graph types are used in EDA to show the relationship between two or more quantitative variables?
- A. Stem-and-leaf plot
- B. Histogram
- C. Heat map
- D. Scatter plot
- E. Box plot
Answer: C,D
NEW QUESTION # 41
When would you use AutoAI to select algorithms for your model?
- A. When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
- B. When the model requirements are extremely specific and no standard algorithm fits.
- C. When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
- D. Only when working with small datasets due to processing limitations.
Answer: A
NEW QUESTION # 42
Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
- A. Keras
- B. PyTorch
- C. Pandas
- D. TensorFlow
Answer: C
NEW QUESTION # 43
What is a critical consideration when selecting the right model class for a given problem?
- A. The nature of the problem (e.g., classification, regression) and the characteristics of the data.
- B. The model's ability to produce results quickly, regardless of accuracy.
- C. The theoretical complexity of the model, with more complex models always being preferred.
- D. The availability of high-performance computing resources.
Answer: A
NEW QUESTION # 44
Which statement best differentiates machine learning from deep learning?
- A. Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
- B. Deep learning algorithms require less data to learn.
- C. Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
- D. Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.
Answer: C
NEW QUESTION # 45
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Transforming data exclusively in cloud environments
- B. Ensuring data quality and consistency throughout the process
- C. Extracting the least amount of data for simplicity
- D. Loading data into a single, centralized database for analysis
Answer: B
NEW QUESTION # 46
What is the primary purpose of partitioning data into training and test sets?
- A. To increase the computational efficiency of model training
- B. To ensure that the model gets exposed to all possible data scenarios during training
- C. To evaluate the model's performance on unseen data
- D. To maximize the accuracy of the model by using all data for training
Answer: C
NEW QUESTION # 47
Which hyperparameter is NOT commonly adjusted in a deep learning model?
- A. Number of layers
- B. The color of the model's output
- C. Learning rate
- D. Activation function
Answer: B
NEW QUESTION # 48
When comparing models to choose the best one, which factor is least likely to be considered?
- A. The performance of the model on validation data
- B. The color scheme of the model's output visualizations
- C. The explainability of the model's predictions
- D. The complexity of the model
Answer: B
NEW QUESTION # 49
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