AIP-210 Exam Dumps - PDF Questions and Testing Engine
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CertNexus AIP-210 Exam Syllabus Topics:
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NEW QUESTION # 41
When should you use semi-supervised learning? (Select two.)
- A. Labeling data is challenging and expensive.
- B. A small set of labeled data is biased toward one class.
- C. There is a large amount of labeled data to be used for predictions.
- D. There is a large amount of unlabeled data to be used for predictions.
- E. A small set of labeled data is available but not representative of the entire distribution.
Answer: A,D
Explanation:
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can be useful when:
* Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. 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.
* There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. 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 # 42
You create a prediction model with 96% accuracy. While the model's true positive rate (TPR) is performing well at 99%, the true negative rate (TNR) is only 50%. Your supervisor tells you that the TNR needs to be higher, even if it decreases the TPR. Upon further inspection, you notice that the vast majority of your data is truly positive.
What method could help address your issue?
- A. Oversampling
- B. Quality filtering
- C. Normalization
- D. Principal components analysis
Answer: A
Explanation:
Oversampling is a method that can help address the issue of imbalanced data, which is when one class is much more frequent than the other in the dataset. This can cause the model to be biased towards the majority class and have a low true negative rate. Oversampling involves creating synthetic samples of the minority class or replicating existing samples to balance the class distribution. This can help the model learn more from the minority class and improve the true negative rate. References: [Handling imbalanced datasets in machine learning], [Oversampling and undersampling in data analysis - Wikipedia]
NEW QUESTION # 43
Which of the following models are text vectorization methods? (Select two.)
- A. Skip-gram
- B. Tokenization
- C. t-SNE
- D. Lemmatization
- E. TF-IDF
- F. PCA
Answer: A,E
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 # 44
Which of the following items should be included in a handover to the end user to enable them to use and run a trained model on their own system? (Select three.)
- A. README document
- B. Information on the folder structure in your local machine
- C. Sample input and output data files
- D. Link to a GitHub repository of the codebase
- E. Intermediate data files
Answer: A,C,D
Explanation:
A handover is the process of transferring the ownership and responsibility of an ML system from one party to another, such as from the developers to the end users. A handover should include all the necessary information and resources that enable the end users to use and run a trained model on their own system. Some of the items that should be included in a handover are:
* Link to a GitHub repository of the codebase: A GitHub repository is an online platform that hosts the source code and version control of an ML system. A link to a GitHub repository can provide the end users with access to the latest and most updated version of the codebase, as well as the history and documentation of the changes made to the code.
* README document: A README document is a text file that provides an overview and instructions for an ML system. A README document can include information such as the purpose, features, requirements, installation, usage, testing, troubleshooting, and license of the system.
* Sample input and output data files: Sample input and output data files are data files that contain examples of valid inputs and expected outputs for an ML system. Sample input and output data files can help the end users understand how to use and run the system, as well as verify its functionality and performance.
NEW QUESTION # 45
You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?
- A. XGBoost
- B. Logistic regression
- C. Random forest
- D. Decision tree
Answer: C
Explanation:
Explanation
Random forest is an algorithm that is ideal to prevent overfitting when using a dataset with many features and a small sample size. Random forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model. Random forest can prevent overfitting by introducing randomness and diversity into the model, such as by using bootstrap sampling (sampling with replacement) to create different subsets of data for each tree, or by using feature selection (choosing a random subset of features) to split each node in a tree.
NEW QUESTION # 46
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 # 47
Which of the following is a common negative side effect of not using regularization?
- A. Higher compute resources
- B. Overfitting
- C. Slow convergence time
- D. Low test accuracy
Answer: B
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 # 48
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. Independence
- B. Equality of variance (Homoscedastidty)
- C. Linearity
- D. Normality
Answer: A
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 # 49
Which of the following describes a neural network without an activation function?
- A. An unsupervised learning technique
- B. A radial basis function kernel
- C. A form of a quantile regression
- D. A form of a linear regression
Answer: D
Explanation:
A neural network without an activation function is equivalent to a form of a linear regression. A neural network is a computational model that consists of layers of interconnected nodes (neurons) that process inputs and produce outputs. An activation function is a function that determines the output of a neuron 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. Without an activation function, a neural network becomes a linear combination of inputs and weights, which is essentially a linear regression model.
NEW QUESTION # 50
We are using the k-nearest neighbors algorithm to classify the new data points. The features are on different scales.
Which method can help us to solve this problem?
- A. Log transformation
- B. Square-root transformation
- C. Normalization
- D. Standardization
Answer: C
Explanation:
Normalization is a method that can help us to solve the problem of features being on different scales when using the k-nearest neighbors algorithm. Normalization is a technique that rescales the values of features to a common range, such as [0, 1] or [-1, 1]. Normalization can help reduce the influence or dominance of some features over others, as well as improve the accuracy and performance of the algorithm2.
NEW QUESTION # 51
Which of the following unsupervised learning models can a bank use for fraud detection?
- A. k-means
- B. Anomaly detection
- C. Hierarchical clustering
- D. DB5CAN
Answer: B
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 # 52
Which three security measures could be applied in different ML workflow stages to defend them against malicious activities? (Select three.)
- A. Use Secrets Manager to protect credentials.
- B. Use data encryption.
- C. Use max privilege to control access to ML artifacts.
- D. Monitor model degradation.
- E. Launch ML Instances In a virtual private cloud (VPC).
- F. Disable logging for model access.
Answer: A,B,E
Explanation:
Explanation
Security measures can be applied in different ML workflow stages to defend them against malicious activities, such as data theft, model tampering, or adversarial attacks. Some of the security measures are:
Launch ML Instances In a virtual private cloud (VPC): A VPC is a logically isolated section of a cloud provider's network that allows users to launch and control their own resources. By launching ML instances in a VPC, users can enhance the security and privacy of their data and models, as well as restrict the access and traffic to and from the instances.
Use data encryption: Data encryption is the process of transforming data into an unreadable format using a secret key or algorithm. Data encryption can protect the confidentiality, integrity, and availability of data at rest (stored in databases or files) or in transit (transferred over networks). Data encryption can prevent unauthorized access, modification, or leakage of sensitive data.
Use Secrets Manager to protect credentials: Secrets Manager is a service that helps users securely store, manage, and retrieve secrets, such as passwords, API keys, tokens, or certificates. Secrets Manager can help users protect their credentials from unauthorized access or exposure, as well as rotate them automatically to comply with security policies.
NEW QUESTION # 53
Which of the following is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data?
- A. Sarbanes Oxley (SOX)
- B. ISO/IEC 27001
- C. General Data Protection Regulation (GDPR)
- D. PCIDSS
Answer: C
Explanation:
The General Data Protection Regulation (GDPR) is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data. The GDPR applies to any organization that processes personal data of individuals in the European Union (EU), regardless of where the organization is located. The GDPR grants individuals rights over their personal data, such as the right to access, rectify, erase, restrict, or object to its processing. The GDPR also imposes obligations on organizations that process personal data, such as the duty to obtain consent, conduct data protection impact assessments, implement data protection by design and by default, and ensure accountability and transparency. The GDPR also addresses some specific issues related to AI, such as automated decision-making, profiling, and data portability.
NEW QUESTION # 54
Which of the following approaches is best if a limited portion of your training data is labeled?
- A. Semi-supervised learning
- B. Probabilistic clustering
- C. Reinforcement learning
- D. Dimensionality reduction
Answer: A
Explanation:
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 # 55
Which of the following algorithms is an example of unsupervised learning?
- A. Neural networks
- B. Principal components analysis
- C. Random forest
- D. Ridge regression
Answer: B
Explanation:
Explanation
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining. Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.
NEW QUESTION # 56
Which two encoders can be used to transform categorical data into numerical features? (Select two.)
- A. Count Encoder
- B. Median Encoder
- C. Log Encoder
- D. One-Hot Encoder
- E. Mean Encoder
Answer: A,D
NEW QUESTION # 57
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Data size
- B. Scalability
- C. Complexity
- D. Explainability
- E. Portability
Answer: B,D
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 # 58
What is the primary benefit of the Federated Learning approach to machine learning?
- A. It does not require a labeled dataset to solve supervised learning problems.
- B. It uses large, centralized data stores to train complex machine learning models.
- C. It protects the privacy of the user's data while providing well-trained models.
- D. It requires less computation to train the same model using a traditional approach.
Answer: C
Explanation:
Federated learning is a distributed approach to machine learning that allows multiple parties to collaboratively train a model without sharing their data with each other or a central server. This protects the privacy of the user's data while still enabling well-trained models that can benefit from diverse and large-scale datasets.
References: [Federated Learning - Wikipedia], [Federated Learning for Mobile Keyboard Prediction - Google AI Blog]
NEW QUESTION # 59
Which of the following is the primary purpose of hyperparameter optimization?
- A. Controls the learning process of a given algorithm
- B. Improves model interpretability
- C. Increases recall over precision
- D. Makes models easier to explain to business stakeholders
Answer: A
Explanation:
Explanation
Hyperparameter optimization is the process of finding the optimal values for hyperparameters that control the learning process of a given algorithm. Hyperparameters are parameters that are not learned by the algorithm but are set by the user before training. Hyperparameters can affect the performance and behavior of the algorithm, such as its speed, accuracy, complexity, or generalization. Hyperparameter optimization can help improve the efficiency and effectiveness of the algorithm by tuning its hyperparameters to achieve the best results.
NEW QUESTION # 60
Which of the following best describes distributed artificial intelligence?
- A. It uses a centralized system to speak to decentralized nodes.
- B. It intelligently pre-distributes the weight of starting a neural network.
- C. It relies on a distributed system that performs robust computations across a network of unreliable nodes.
- D. It does not require hyperparemeter tuning because the distributed nature accounts for the bias.
Answer: C
Explanation:
Explanation
Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that studies how multiple intelligent agents can coordinate and cooperate to achieve a common goal or solve a complex problem. DAI relies on a distributed system that performs robust computations across a network of unreliable nodes, such as sensors, robots, or humans. DAI can handle large-scale, dynamic, and uncertain environments that are beyond the capabilities of a single agent. References: [Distributed artificial intelligence - Wikipedia], [Distributed Artificial Intelligence: An Overview]
NEW QUESTION # 61
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