Dec 07, 2024 Newest C1000-154 Exam Dumps – Achieve Success in Actual C1000-154 Exam
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IBM C1000-154 exam, also known as the IBM Watson Data Scientist v1 exam, is designed to test the knowledge and skills of professionals in the field of data science. IBM Watson Data Scientist v1 certification exam is part of IBM's Watson certification program, which includes a range of certifications related to artificial intelligence, machine learning, and data science. The C1000-154 exam focuses specifically on assessing the candidate's ability to work with IBM Watson Studio and IBM Watson Knowledge Catalog, as well as their knowledge of data science concepts and techniques.
NEW QUESTION # 34
Which of the following is true regarding cross-validation?
- A. It should be avoided as it leads to overfitting.
- B. It helps in identifying the model's performance variability across different data splits.
- C. It decreases the variability of the model performance estimation.
- D. It involves training the model on the entire dataset at once.
Answer: B,C
NEW QUESTION # 35
In Cognos Analytics, which two features distinguish stories from dashboards?
- A. Stories are text-based and do not contain visualizations.
- B. Stories can be embedded in websites or documents.
- C. Stories convey a conclusion.
- D. Stories provide a narrative over time.
- E. Stories automatically load different filters for different users.
Answer: C,D
NEW QUESTION # 36
When helping businesses articulate and define problems, what is an essential first step?
- A. Selecting the analytical techniques
- B. Identifying potential data sources
- C. Establishing a clear problem statement
- D. Defining key performance indicators (KPIs)
Answer: C
NEW QUESTION # 37
What is data leakage in the context of model training?
- A. Leakage of sensitive information due to poor data handling practices
- B. Loss of data during the splitting process
- C. When data from outside the training dataset is accidentally included in the training process
- D. A situation where the test data is not available
Answer: C
NEW QUESTION # 38
Which statement best differentiates machine learning from deep learning?
- A. Deep learning algorithms require less data to learn.
- B. Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
- C. Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
- D. Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.
Answer: B
NEW QUESTION # 39
In defining a business problem, what is essential to align with the stakeholders?
- A. Business objectives
- B. Project milestones
- C. Technical requirements
- D. Data sources
Answer: A
NEW QUESTION # 40
What is a key disadvantage of using Grid Search for hyperparameter tuning?
- A. It requires no prior knowledge of the hyperparameters
- B. It is unable to handle discrete parameters
- C. It can be computationally expensive and time-consuming due to its exhaustive nature
- D. It is too quick and may miss out on evaluating some hyperparameters
Answer: C
NEW QUESTION # 41
When would you use AutoAI to select algorithms for your model?
- A. Only when working with small datasets due to processing limitations.
- B. When the model requirements are extremely specific and no standard algorithm fits.
- C. When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
- D. When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
Answer: C
NEW QUESTION # 42
Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
- A. Recall
- B. F1-score
- C. Precision
- D. Accuracy
Answer: B
NEW QUESTION # 43
Why is it important to create data splits that are reproducible?
- A. To ensure that each model run can be exactly replicated for verification and comparison
- B. To use more data for testing than for training
- C. To guarantee that the model will perform with 100% accuracy on unseen data
- D. To allow for larger test sets for more comprehensive testing
Answer: A
NEW QUESTION # 44
Profiling and visualizing data using Watson tools primarily helps in:
- A. Increasing the quantity of data for analysis
- B. Creating aesthetically pleasing presentations without regard to data relevance
- C. Simplifying the data collection process without analyzing quality
- D. Identifying patterns, outliers, and insights in the data
Answer: D
NEW QUESTION # 45
The first step in performing exploratory data analysis (EDA) typically involves:
- A. Choosing a color palette for data visualization
- B. Selecting a random sample of data to analyze
- C. Connecting to as many data sources as possible
- D. Determining the hypothesis for the analysis
Answer: D
NEW QUESTION # 46
When deploying models in Watson Machine Learning, what is essential for ensuring the models perform as expected in production?
- A. Continuous monitoring and evaluation of model performance
- B. Limiting access to the model to a few select users
- C. Using the highest number of resources for every model
- D. Deployment without any security measures
Answer: A
NEW QUESTION # 47
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. Data Understanding
- B. Modeling
- C. Data Preparation
- D. Business Understanding
Answer: C
NEW QUESTION # 48
Which statement describes bagging?
- A. Building models and using their output as features into a final model.
- B. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
- C. Building models in parallel and aggregating their predictions to select the final prediction.
- D. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
Answer: C
NEW QUESTION # 49
Given the Confusion matrix below, which is the formula for specificity?
- A. TN/(TN + FP)
- B. TP/(FN + TP)
- C. (TP + TN)/(FN + FP + TN + TP)
- D. TP/(FP + TP)
Answer: A
NEW QUESTION # 50
Which of the following is true about the AUC measure in the context of classification models?
- A. It is less useful when the classes are highly imbalanced.
- B. It represents the degree of separability between classes.
- C. It indicates the number of false positives.
- D. It measures the model's accuracy using a single threshold.
Answer: B
NEW QUESTION # 51
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