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CertNexus AIP-210 Exam Syllabus Topics:
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NEW QUESTION # 10
Which of the following text vectorization methods is appropriate and correctly defined for an English-to-Spanish translation machine?
- A. Using TF-IDF because in translation machines, we need to consider the order of the words.
- B. Using Word2vec because in translation machines, we do not care about the order of the words.
- C. Using Word2vec because in translation machines, we need to consider the order of the words.
- D. Using TF-IDF because in translation machines, we do not care about the order of the words.
Answer: C
Explanation:
Explanation
Text vectorization is a technique that converts text into numerical vectors that can be used by machine learning models. Text vectorization can use different methods to represent text features, such as word frequency, word order, word meaning, or word context. Some of the common text vectorization methods are:
TF-IDF: TF-IDF (term frequency-inverse document frequency) is a method that assigns a weight to each word based on its frequency in a document and its rarity across a collection of documents. TF-IDF can capture the importance and relevance of words for a given topic or domain, but it does not consider the order or meaning of words.
Word2vec: Word2vec is a method that learns a 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, as well as preserve the order of words.
For an English-to-Spanish translation machine, using Word2vec would be appropriate and correctly defined, because in translation machines, we need to consider the order of the words, as well as their meaning and context.
NEW QUESTION # 11
In a self-driving car company, ML engineers want to develop a model for dynamic pathing. Which of following approaches would be optimal for this task?
- A. Reinforcement learning
- B. Dijkstra Algorithm
- C. Supervised Learning.
- D. Unsupervised Learning
Answer: A
Explanation:
Explanation
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment.
Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .
NEW QUESTION # 12
Which of the following can take a question in natural language and return a precise answer to the question?
- A. Databricks
- B. IBM Watson
- C. Pandas
- D. Spark ML
Answer: B
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 # 13
R-squared is a statistical measure that:
- A. Expresses the extent to which two variables are linearly related.
- B. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
- C. Represents the extent to which two random variables vary together.
- D. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
Answer: B
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 # 14
Workflow design patterns for the machine learning pipelines:
- A. Represent a pipeline with directed acyclic graph (DAG).
- B. Seek to simplify the management of machine learning features.
- C. Separate inputs from features.
- D. Aim to explain how the machine learning model works.
Answer: A
Explanation:
Explanation
Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.
NEW QUESTION # 15
Which of the following is a common negative side effect of not using regularization?
- A. Overfitting
- B. Slow convergence time
- C. Low test accuracy
- D. Higher compute resources
Answer: A
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 # 16
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
- A. Model validation occurs before model evaluation.
- B. Model evaluation is defined as an external component.
- C. Model evaluation and validation are the same.
- D. Model validation is defined as a set of tasks to confirm the model performs as expected.
Answer: D
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 # 17
Which two of the following statements about the beta value in an A/B test are accurate? (Select two.)
- A. The statistical power of a test is the inverse of the Beta value, or 1 - Beta.
- B. The Beta in an Alpha/Beta test represents one of the two variants of the A/B test.
- C. The Beta value is the rate of type II errors for the test.
- D. The Beta value is the rate of type I errors for the test.
Answer: C
Explanation:
Explanation
The Beta value in an A/B test is the probability of making a type II error, which is failing to reject the null hypothesis when it is false. The statistical power of a test is the probability of correctly rejecting the null hypothesis when it is false, which is equal to 1 - Beta. References: Formulas for Bayesian A/B Testing - Evan Miller, The Practical Guide To AB testing statistics | Convertize
NEW QUESTION # 18
Which of the following is NOT an activation function?
- A. Hyperbolic tangent
- B. Sigmoid
- C. Additive
- D. ReLU
Answer: C
Explanation:
Explanation
An activation function is a function that determines the output of a neuron in a neural network based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Some of the common activation functions are:
Sigmoid: A sigmoid function is a function that maps any real value to a value between 0 and 1. It has an S-shaped curve and is often used for binary classification or probability estimation.
Hyperbolic tangent: A hyperbolic tangent function is a function that maps any real value to a value between -1 and 1. It has a similar shape to the sigmoid function but is symmetric around the origin. It is often used for regression or classification problems.
ReLU: A ReLU (rectified linear unit) function is a function that maps any negative value to 0 and any positive value to itself. It has a piecewise linear shape and is often used for hidden layers in deep neural networks.
Additive is not an activation function, but rather a term that describes a property of some functions. Additive functions are functions that satisfy the condition f(x+y) = f(x) + f(y) for any x and y. Additive functions are linear functions, which means they have a constant slope and do not introduce non-linearity.
NEW QUESTION # 19
Which of the following describes a benefit of machine learning for solving business problems?
- A. Increasing the quantity of original data
- B. Improving the constraint of the problem
- C. Improving the quality of original data
- D. Increasing the speed of analysis
Answer: D
Explanation:
Explanation
Increasing the speed of analysis is a benefit of machine learning for solving business problems. Machine learning is a branch of artificial intelligence that involves creating systems that can learn from data and make predictions or decisions. Machine learning can help increase the speed of analysis by automating and optimizing various tasks, such as data processing, feature extraction, model training, model evaluation, or model deployment. Machine learning can also help handle large and complex data sets that may be difficult or impractical to analyze manually or with traditional methods.
NEW QUESTION # 20
In general, models that perform their tasks:
- A. More accurately are less robust against adversarial attacks.
- B. More accurately are neither more nor less robust against adversarial attacks.
- C. Less accurately are neither more nor less robust against adversarial attacks.
- D. Less accurately are less robust against adversarial attacks.
Answer: A
Explanation:
Explanation
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data. References: [Adversarial machine learning - Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]
NEW QUESTION # 21
A change in the relationship between the target variable and input features is
- A. covariate shift.
- B. data drift.
- C. model decay.
- D. concept drift.
Answer: D
Explanation:
Explanation
Concept drift, also known as model drift, occurs when the task that the model was designed to perform changes over time. For example, imagine that a machine learning model was trained to detect spam emails based on the content of the email. If the types of spam emails that people receive change significantly, the model may no longer be able to accurately detect spam. References: Understanding Data Drift and Model Drift: Drift Detection in Python | DataCamp, Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift
NEW QUESTION # 22
In which of the following scenarios is lasso regression preferable over ridge regression?
- A. The sample size is much larger than the number of features.
- B. There is high collinearity among some of the features associated with the dependent variable.
- C. There are many features with no association with the dependent variable.
- D. The number of features is much larger than the sample size.
Answer: C
Explanation:
Explanation
Lasso regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of the coefficients to zero, which effectively eliminates some of the features from the model. Lasso regression is preferable over ridge regression when there are many features with no association with the dependent variable, as it can perform feature selection and reduce the complexity and noise of the model.
NEW QUESTION # 23
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?
- A. Bagging
- B. Boosting
- C. Stacking
- D. Weighted class
Answer: D
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 # 24
Which of the following is the correct definition of the quality criteria that describes completeness?
- A. The degree to which a set of measures are specified using the same units of measure in all systems.
- B. The degree to which all required measures are known.
- C. The degree to which a set of measures are equivalent across systems.
- D. The degree to which the measures conform to defined business rules or constraints.
Answer: B
Explanation:
Explanation
Completeness is a quality criterion that describes the degree to which all required measures are known.
Completeness can help assess the coverage and availability of data for a given purpose or analysis.
Completeness can be measured by comparing the actual number of measures with the expected number of measures, or by identifying and counting any missing, null, or unknown values in the data.
NEW QUESTION # 25
What is Word2vec?
- A. A bag of words.
- B. A word embedding method that finds characteristics of words in a very large number of documents.
- C. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
- D. A matrix of how frequently words appear in a group of documents.
Answer: B
Explanation:
Explanation
Word2vec is a word embedding method that finds characteristics of words in a very large number of documents. Word embedding is a technique that converts words into numerical vectors that represent their meaning, usage, or context. Word2vec 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 associations1.
NEW QUESTION # 26
Which of the following unsupervised learning models can a bank use for fraud detection?
- A. k-means
- B. DB5CAN
- C. Anomaly detection
- D. Hierarchical clustering
Answer: C
Explanation:
Explanation
Anomaly detection is an unsupervised learning technique that identifies outliers or abnormal patterns in data, which can be useful for fraud detection. Anomaly detection algorithms can learn the normal behavior of transactions and flag the ones that deviate significantly from the norm, indicating possible fraud.
NEW QUESTION # 27
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. Divide all the Y values by the standard deviation of Y.
- B. Subtract the mean of Y from all the Y values.
- C. Explore the data for outliers.
- D. Add 1 to all of the Y values.
Answer: D
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 # 28
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A. Bag of bigrams (2 letter pairs)
- B. Clustering similar words and representing words by group membership
- C. Bag of words model with TF-IDF
- D. Word2Vec algorithm
Answer: A
Explanation:
Explanation
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word "hello" would be represented as
{"he": 1, "el": 1, "ll": 1, "lo": 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as "th" in English or "ch" in German .
NEW QUESTION # 29
In general, models that perform their tasks:
- A. More accurately are less robust against adversarial attacks.
- B. More accurately are neither more nor less robust against adversarial attacks.
- C. Less accurately are neither more nor less robust against adversarial attacks.
- D. Less accurately are less robust against adversarial attacks.
Answer: A
Explanation:
Explanation
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data. References: [Adversarial machine learning - Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]
NEW QUESTION # 30
You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive treatment. The treatment is cheap and has no side effects. Patients with the disease who don't receive treatment have a high risk of mortality.
It is of primary importance that your diagnostic tool has which of the following?
- A. Low false negative rate
- B. High positive predictive value
- C. High negative predictive value
- D. Low false positive rate
Answer: A
Explanation:
Explanation
A false negative is an error where a positive case (belonging to the target class) is incorrectly predicted as negative (not belonging to the target class). A false negative rate is the ratio of false negatives to all actual positive cases. A low false negative rate means that most of the positive cases are correctly identified by the classifier.
For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can receive treatment, it is of primary importance that it has a low false negative rate. This is because false negatives can have serious consequences for patients who have the disease but do not receive treatment, such as increased risk of mortality or complications. A low false negative rate can ensure that most patients who have the disease are diagnosed correctly and receive timely treatment.
NEW QUESTION # 31
Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)
- A. If the data is only collected once.
- B. If it was with consent of the person it is collected from.
- C. If it was collected accidentally.
- D. If it was requested by the authority of law.
- E. If it is for a good cause.
Answer: B,D
Explanation:
Explanation
Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.
NEW QUESTION # 32
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