Latest Jan 16, 2024 AIP-210 Brain Dump: A Study Guide with Tips & Tricks for passing Exam
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NEW QUESTION # 34
You train a neural network model with two layers, each layer having four nodes, and realize that the model is underfit. Which of the actions below will NOT work to fix this underfitting?
- A. Increase the complexity of the model
- B. Train the model for more epochs
- C. Get more training data
- D. Add features to training data
Answer: C
Explanation:
Explanation
Underfitting is a problem that occurs when a model learns too little from the training data and fails to capture the underlying complexity or structure of the data. Underfitting can result from using insufficient or irrelevant features, a low complexity of the model, or a lack of training data. Underfitting can reduce the accuracy and generalization of the model, as it may produce oversimplified or inaccurate predictions. Some of the ways to fix underfitting are:
Add features to training data: Adding more features or variables to the training data can help increase the information and diversity of the data, which can help the model learn more complex patterns and relationships.
Increase the complexity of the model: Increasing the complexity of the model can help increase its expressive power and flexibility, which can help it fit better to the data. For example, adding more layers or nodes to a neural network can increase its complexity.
Train the model for more epochs: Training the model for more epochs can help increase its learning ability and convergence, which can help it optimize its parameters and reduce its error.
Getting more training data will not work to fix underfitting, as it will not change the complexity or structure of the data or the model. Getting more training data may help with overfitting, which is when a model learns too much from the training data and fails to generalize well to new or unseen data.
NEW QUESTION # 35
Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression.
What should you do before log-transforming Y?
- A. Add 1 to all of the Y values.
- B. Explore the data for outliers.
- C. Subtract the mean of Y from all the Y values.
- D. Divide all the Y values by the standard deviation of Y.
Answer: A
Explanation:
Explanation
Before log-transforming Y, we should add 1 to all of the Y values. This is because log transformation is undefined for zero or negative values, and some of the Y values may be zero. Adding 1 to all of the Y values can avoid this problem and ensure that the log transformation is valid and meaningful. Adding 1 to all of the Y values is also known as a log-plus-one transformation.
NEW QUESTION # 36
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
- A. Model validation is defined as a set of tasks to confirm the model performs as expected.
- B. Model evaluation is defined as an external component.
- C. Model evaluation and validation are the same.
- D. Model validation occurs before model evaluation.
Answer: A
Explanation:
Explanation
Model validation is the process of checking whether the model meets the specified requirements and quality standards. It involves testing the model on a validation dataset, which is different from the training and testing datasets, and evaluating the model performance using appropriate metrics. References: Overview of ML Pipelines | Machine Learning, MLOps: Continuous delivery and automation pipelines in machine learning
NEW QUESTION # 37
What is the open framework designed to help detect, respond to, and remediate threats in ML systems?
- A. Threat Susceptibility Matrix
- B. Adversarial ML Threat Matrix
- C. MITRE ATT&CK Matrix
- D. OWASP Threat and Safeguard Matrix
Answer: B
Explanation:
Explanation
The Adversarial ML Threat Matrix is an open framework designed to help detect, respond to, and remediate threats in ML systems. The Adversarial ML Threat Matrix is inspired by the MITRE ATT&CK Matrix1, which is a framework for describing cyberattacks across various stages of an attack lifecycle. The Adversarial ML Threat Matrix adapts this framework to address specific threats and vulnerabilities in ML systems, such as data poisoning, model stealing, model evasion, or model inversion2. The Adversarial ML Threat Matrix provides a structured way to organize and classify adversarial techniques, tactics, procedures, examples, and mitigations for ML systems2.
NEW QUESTION # 38
Which of the following is a common negative side effect of not using regularization?
- A. Higher compute resources
- B. Low test accuracy
- C. Overfitting
- D. Slow convergence time
Answer: C
Explanation:
Explanation
Overfitting is a common negative side effect of not using regularization. Regularization is a technique that reduces the complexity of a model by adding a penalty term to the loss function, which prevents the model from learning too many parameters that may fit the noise in the training data. Overfitting occurs when the model performs well on the training data but poorly on the test data or new data, because it has memorized the training data and cannot generalize well. References: Regularization (mathematics) - Wikipedia, Overfitting in Machine Learning: What It Is and How to Prevent It
NEW QUESTION # 39
Which of the following can take a question in natural language and return a precise answer to the question?
- A. Databricks
- B. Spark ML
- C. IBM Watson
- D. Pandas
Answer: C
Explanation:
Explanation
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.
NEW QUESTION # 40
Which type of regression represents the following formula: y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable?
- A. Lasso regression
- B. Linear regression
- C. Polynomial regression
- D. Ridge regression
Answer: B
NEW QUESTION # 41
R-squared is a statistical measure that:
- A. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
- B. Represents the extent to which two random variables vary together.
- C. Expresses the extent to which two variables are linearly related.
- D. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
Answer: D
Explanation:
Explanation
R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by dividing the explained variance by the total variance. The explained variance is the amount of variation in the dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1 means perfect fit.
NEW QUESTION # 42
Which of the following statements are true regarding highly interpretable models? (Select two.)
- A. They usually compromise on model accuracy for the sake of interpretability.
- B. They are usually easier to explain to business stakeholders.
- C. They are usually referred to as "black box" models.
- D. They are usually binary classifiers.
- E. They are usually very good at solving non-linear problems.
Answer: A,B
Explanation:
Explanation
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.
NEW QUESTION # 43
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?
- A. As new resources become available
- B. When the input format changes
- C. Once a month
- D. When the input volume changes
Answer: B
Explanation:
Explanation
The input format is the way that the data is structured, organized, and presented to the model. For example, the input format could be a CSV file, an image file, or a JSON object. The input format can affect how the model interprets and processes the data, and therefore how it makes predictions. When the input format changes, it may require retraining the model to adapt to the new format and ensure its accuracy and reliability. For example, if the weather prediction application switches from using numerical values to categorical values for some features, such as wind direction or cloud cover, it may need to retrain the model to handle these changes
.
NEW QUESTION # 44
Word Embedding describes a task in natural language processing (NLP) where:
- A. Words are featurized by taking a matrix of bigram counts.
- B. Words are featurized by taking a histogram of letter counts.
- C. Words are grouped together into clusters and then represented by word cluster membership.
- D. Words are converted into numerical vectors.
Answer: D
Explanation:
Explanation
Word embedding is a task in natural language processing (NLP) where words are converted into numerical vectors that represent their meaning, usage, or context. Word embedding can help reduce the dimensionality and sparsity of text data, as well as enable various operations and comparisons among words based on their vector representations. Some of the common methods for word embedding are:
One-hot encoding: One-hot encoding is a method that assigns a unique binary vector to each word in a vocabulary. The vector has only one element with a value of 1 (the hot bit) and the rest with a value of
0. One-hot encoding can create distinct and orthogonal vectors for each word, but it does not capture any semantic or syntactic information about words.
Word2vec: Word2vec is a method that learns a dense and continuous vector representation for each word based on its context in a large corpus of text. Word2vec can capture the semantic and syntactic similarity and relationships among words, such as synonyms, antonyms, analogies, or associations.
GloVe: GloVe (Global Vectors for Word Representation) is a method that combines the advantages of count-based methods (such as TF-IDF) and predictive methods (such as Word2vec) to create word vectors. GloVe can leverage both global and local information from a large corpus of text to capture the co-occurrence patterns and probabilities of words.
NEW QUESTION # 45
For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the
10-year period, with age as the dependent variable and the biomarkers as predictors.
Which assumption of linear regression is being violated?
- A. Linearity
- B. Equality of variance (Homoscedastidty)
- C. Independence
- D. Normality
Answer: C
Explanation:
Explanation
Independence is an assumption of linear regression that states that the errors (residuals) of the model are independent of each other, meaning that they are not correlated or influenced by previous or subsequent errors.
Independence can be violated when the data has serial correlation or autocorrelation, which means that the value of a variable at a given time depends on its previous or future values. This can happen when the data is collected over time (time series) or over space (spatial data). In this case, the data is collected over time from a group of subjects, which may introduce serial correlation among the errors.
NEW QUESTION # 46
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?
- A. Stacking
- B. Bagging
- C. Weighted class
- D. Boosting
Answer: C
Explanation:
Explanation
Weighted class is a technique to rebalance a dataset by assigning different weights to each class, according to their frequency in the dataset. The weights are inversely proportional to the class frequency, meaning that rare classes have higher weights and common classes have lower weights. This helps to reduce the bias towards the majority class and improve the model performance on the minority class. References: 4. Data Validation - Building Machine Learning Pipelines, A guide to React design patterns - LogRocket Blog
NEW QUESTION # 47
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?
- A. Incident
- B. Code Repository
- C. Configuration Management
- D. Asset
Answer: A
Explanation:
Explanation
An incident database is a database that is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems. An incident database collects and stores information about incidents or events where AI systems have caused or contributed to negative outcomes or harms, such as accidents, errors, biases, discriminations, or violations. An incident database can help identify patterns, trends, causes, impacts, and solutions for AI-related incidents, as well as provide guidance and best practices for preventing or mitigating future incidents.
NEW QUESTION # 48
For a particular classification problem, you are tasked with determining the best algorithm among SVM, random forest, K-nearest neighbors, and a deep neural network. Each of the algorithms has similar accuracy on your data. The stakeholders indicate that they need a model that can convey each feature's relative contribution to the model's accuracy. Which is the best algorithm for this use case?
- A. SVM
- B. Deep neural network
- C. Random forest
- D. K-nearest neighbors
Answer: C
Explanation:
Explanation
Random forest is an ensemble learning method that combines multiple decision trees to create a more accurate and robust classifier or regressor. Random forest can convey each feature's relative contribution to the model's accuracy by measuring how much the prediction error increases when a feature is randomly permuted. This metric is called feature importance or Gini importance. Random forest can also provide insights into the interactions and dependencies among features by visualizing the decision trees .
NEW QUESTION # 49
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