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CertNexus AIP-210 Exam Syllabus Topics:
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NEW QUESTION # 21
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. K-nearest neighbors
- C. Random forest
- D. Deep neural network
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 # 22
Which two encoders can be used to transform categorical data into numerical features? (Select two.)
- A. Mean Encoder
- B. Log Encoder
- C. Count Encoder
- D. Median Encoder
- E. One-Hot Encoder
Answer: C,E
NEW QUESTION # 23
A data scientist is tasked to extract business intelligence from primary data captured from the public. Which of the following is the most important aspect that the scientist cannot forget to include?
- A. Cybersecurity
- B. Data security
- C. Cyberprotection
- D. Data privacy
Answer: D
Explanation:
Explanation
Data privacy is the right of individuals to control how their personal data is collected, used, shared, and protected. It also involves complying with relevant laws and regulations that govern the handling of personal data. Data privacy is especially important when extracting business intelligence from primary data captured from the public, as it may contain sensitive or confidential information that could harm the individuals if misused or breached .
NEW QUESTION # 24
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Portability
- B. Complexity
- C. Explainability
- D. Data size
- E. Scalability
Answer: C,E
Explanation:
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment.
Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions.
Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.
NEW QUESTION # 25
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 positive rate
- B. High positive predictive value
- C. High negative predictive value
- D. Low false negative rate
Answer: D
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 # 26
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?
- A. Once a month
- B. When the input volume changes
- C. As new resources become available
- D. When the input format changes
Answer: D
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 # 27
Workflow design patterns for the machine learning pipelines:
- A. Represent a pipeline with directed acyclic graph (DAG).
- B. Aim to explain how the machine learning model works.
- C. Seek to simplify the management of machine learning features.
- D. Separate inputs from features.
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 # 28
In general, models that perform their tasks:
- A. Less accurately are neither more nor less robust against adversarial attacks.
- B. Less accurately are less robust against adversarial attacks.
- C. More accurately are neither more nor less robust against adversarial attacks.
- D. More accurately are less robust against adversarial attacks.
Answer: D
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 # 29
A market research team has ratings from patients who have a chronic disease, on several functional, physical, emotional, and professional needs that stay unmet with the current therapy. The dataset also captures ratings on how the disease affects their day-to-day activities.
A pharmaceutical company is introducing a new therapy to cure the disease and would like to design their marketing campaign such that different groups of patients are targeted with different ads. These groups should ideally consist of patients with similar unmet needs.
Which of the following algorithms should the market research team use to obtain these groups of patients?
- A. Logistic regression
- B. Naive-Bayes
- C. k-means clustering
- D. k-nearest neighbors
Answer: C
Explanation:
k-means clustering is an algorithm that should be used by the market research team to obtain groups of patients with similar unmet needs. k-means clustering is an unsupervised learning technique that partitions the data into k clusters based on the similarity of the features. The algorithm iteratively assigns each data point to the cluster with the nearest centroid and updates the centroid until convergence. k-means clustering can help identify patterns and segments in the data that may not be obvious or intuitive. References: [K-means clustering - Wikipedia], [How to Run K-Means Clustering in Python]
NEW QUESTION # 30
Which of the following tools would you use to create a natural language processing application?
- A. NLTK
- B. AWS DeepRacer
- C. Azure Search
- D. DeepDream
Answer: A
Explanation:
NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .
NEW QUESTION # 31
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 the measures conform to defined business rules or constraints.
- C. The degree to which all required measures are known.
- D. The degree to which a set of measures are equivalent across systems.
Answer: C
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 # 32
Which of the following text vectorization methods is appropriate and correctly defined for an English-to- Spanish translation machine?
- A. Using Word2vec because in translation machines, we need to consider the order of the words.
- B. Using TF-IDF because in translation machines, we need to consider the order of the words.
- C. Using Word2vec because in translation machines, we do not care about the order of the words.
- D. Using TF-IDF because in translation machines, we do not care about the order of the words.
Answer: A
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 # 33
Which of the following approaches is best if a limited portion of your training data is labeled?
- A. Dimensionality reduction
- B. Probabilistic clustering
- C. Reinforcement learning
- D. Semi-supervised learning
Answer: D
Explanation:
Semi-supervised learning is an approach that is best if a limited portion of your training data is labeled. Semi- supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model.
Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.
NEW QUESTION # 34
Which of the following models are text vectorization methods? (Select two.)
- A. Lemmatization
- B. PCA
- C. TF-IDF
- D. Tokenization
- E. t-SNE
- F. Skip-gram
Answer: C,F
Explanation:
Explanation
Skip-gram and TF-IDF are both text vectorization methods that convert text into numerical feature vectors.
Skip-gram is a prediction-based word embedding method that learns vector representations of words from their contexts in a large corpus of text. TF-IDF is a frequency-based word weighting method that assigns scores to words based on their importance in a document and in a corpus of documents. References: Text Vectorization and Word Embedding | Guide to Master NLP (Part 5), What Is Text Vectorization? Everything You Need to Know - deepset
NEW QUESTION # 35
Which of the following describes a benefit of machine learning for solving business problems?
- A. Improving the constraint of the problem
- B. Increasing the speed of analysis
- C. Improving the quality of original data
- D. Increasing the quantity of original data
Answer: B
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 # 36
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?
- A. Once a month
- B. When the input volume changes
- C. As new resources become available
- D. When the input format changes
Answer: D
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 # 37
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. Dijkstra Algorithm
- B. Supervised Learning.
- C. Reinforcement learning
- D. Unsupervised Learning
Answer: C
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 # 38
In general, models that perform their tasks:
- A. Less accurately are neither more nor less robust against adversarial attacks.
- B. Less accurately are less robust against adversarial attacks.
- C. More accurately are neither more nor less robust against adversarial attacks.
- D. More accurately are less robust against adversarial attacks.
Answer: D
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 # 39
Normalization is the transformation of features:
- A. By subtracting from the mean and dividing by the standard deviation.
- B. To different scales from each other.
- C. Into the normal distribution.
- D. So that they are on a similar scale.
Answer: D
Explanation:
Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or
-1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k- nearest neighbors. References: [Feature scaling - Wikipedia], [Normalization vs Standardization - Quantitative analysis]
NEW QUESTION # 40
You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?
- A. Deep learning neural network
- B. Ridge regression
- C. Random forest
- D. Support-vector machine
Answer: B
Explanation:
Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.
NEW QUESTION # 41
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