2026 Easy Success CertNexus AIP-210 Exam in First Try
Best AIP-210 Exam Dumps for the Preparation of Latest Exam Questions
CertNexus AIP-210 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
| Topic 5 |
|
| Topic 6 |
|
NEW QUESTION # 41
When should the model be retrained in the ML pipeline?
- A. Some outliers are detected in live data.
- B. More data become available for the training phase.
- C. A new monitoring component is added.
- D. Concept drift is detected in the pipeline.
Answer: D
Explanation:
Explanation
When concept drift is detected in the pipeline, it means that the model performance has degraded over time due to changes in the underlying data generating process. This requires retraining the model with new data that reflects the current situation and updating the model parameters accordingly. References: Use pipeline parameters to retrain models in the designer - Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing
NEW QUESTION # 42
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. Supervised Learning.
- B. Reinforcement learning
- C. Dijkstra Algorithm
- D. Unsupervised Learning
Answer: B
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 # 43
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. Train the model for more epochs
- B. Increase the complexity of the model
- C. Add features to training data
- D. Get more training data
Answer: D
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 # 44
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. Supervised Learning.
- B. Reinforcement learning
- C. Dijkstra Algorithm
- D. Unsupervised Learning
Answer: B
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 # 45
Which of the following describes a benefit of machine learning for solving business problems?
- A. Improving the quality of original data
- B. Improving the constraint of the problem
- C. Increasing the speed of analysis
- D. Increasing the quantity of original data
Answer: C
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 # 46
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 I errors for the test.
- D. The Beta value is the rate of type II errors for the test.
Answer: D
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 # 47
Which of the following is NOT an activation function?
- A. ReLU
- B. Sigmoid
- C. Hyperbolic tangent
- D. Additive
Answer: D
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 # 48
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 # 49
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 very good at solving non-linear problems.
- D. They are usually binary classifiers.
- E. They are usually referred to as "black box" models.
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 # 50
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
- A. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
- B. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
- C. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- D. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
Answer: B
Explanation:
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
* Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider's infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
* Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
* Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
* Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.
NEW QUESTION # 51
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:
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 # 52
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?
- A. Fill in missing features with the average of observed values for that feature in the entire dataset.
- B. Fill in missing features with random values for that feature in the training set.
- C. Delete entire rows that contain any missing features.
- D. Delete entire columns that contain any missing features.
Answer: A
Explanation:
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.
NEW QUESTION # 53
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?
- A. A decision tree algorithm, because the problem is a classification problem with a small number of features.
- B. k-means, because this is a clustering problem with a small number of features.
- C. Logistic regression, because this is a classification problem and our data is linearly separable.
- D. Neural network model, because this is a classification problem with a large number of features.
Answer: D
Explanation:
Explanation
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]
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
A classifier has been implemented to predict whether or not someone has a specific type of disease.
Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?
- A. Mean squared error
- B. Precision and accuracy
- C. Precision and recall
- D. Recall and explained variance
Answer: C
NEW QUESTION # 56
An HR solutions firm is developing software for staffing agencies that uses machine learning.
The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?
- A. Technical
- B. Automation
- C. Emergent
- D. Preexisting
Answer: D
Explanation:
Explanation
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.
NEW QUESTION # 57
Which of the following scenarios is an example of entanglement in ML pipelines?
- A. Add a new pipeline for retraining the model in the model training step.
- B. Add a new method for drift detection in the model evaluation step.
- C. Change in normalization function in the feature engineering step.
- D. Change the way output is visualized in the monitoring step.
Answer: C
Explanation:
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it.
Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.
NEW QUESTION # 58
Word Embedding describes a task in natural language processing (NLP) where:
- A. Words are grouped together into clusters and then represented by word cluster membership.
- B. Words are featurized by taking a matrix of bigram counts.
- C. Words are featurized by taking a histogram of letter counts.
- 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 # 59
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?
- A. A more complex model
- B. Guaranteed availability of enough space
- C. Reduction in latency
- D. Increase in data bandwidth consumption
Answer: C
Explanation:
Latency is the time delay between a request and a response. Latency can affect the performance and user experience of an application, especially when real-time or near-real-time responses are required. Deploying a deep learning model as an embedded model on edge devices can reduce latency, as the model can run locally on the device without relying on network connectivity or cloud servers. Edge devices are devices that are located at the edge of a network, such as smartphones, tablets, laptops, sensors, cameras, or drones.
NEW QUESTION # 60
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. Polynomial regression
- B. Linear regression
- C. Ridge regression
- D. Lasso regression
Answer: B
NEW QUESTION # 61
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 very good at solving non-linear problems.
- D. They are usually binary classifiers.
- E. They are usually referred to as "black box" models.
Answer: A,B
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 # 62
......
AIP-210 Study Material, Preparation Guide and PDF Download: https://www.examtorrent.com/AIP-210-valid-vce-dumps.html
AIP-210 Actual Questions 100% Same Braindumps with Actual Exam: https://drive.google.com/open?id=18CpamH1Pu03xWyVWR7qMWm-zIikfpaKj
