[Dec 10, 2023] AIP-210 Sample with Accurate & Updated Questions
AIP-210 Exam Info and Free Practice Test | Exam4Tests
NEW QUESTION # 40
When should the model be retrained in the ML pipeline?
- A. Concept drift is detected in the pipeline.
- B. A new monitoring component is added.
- C. More data become available for the training phase.
- D. Some outliers are detected in live data.
Answer: A
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 # 41
What is Word2vec?
- A. A word embedding method that finds characteristics of words in a very large number of documents.
- B. A bag of words.
- 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: A
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 # 42
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. One-Hot Encoder
- B. Log Encoder
- C. Mean Encoder
- D. Median Encoder
- E. Count Encoder
Answer: A,C
Explanation:
Explanation
Encoding is a technique that transforms categorical data into numerical features that can be used by machine learning models. Categorical data are data that have a finite number of possible values or categories, such as gender, color, or country. Encoding can help convert categorical data into a format that is suitable and understandable for machine learning models. Some of the encoding methods that can be used to transform categorical data into numerical features are:
Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the target variable for that category. Mean encoder can capture the relationship between the category and the target variable, but it may cause overfitting or multicollinearity problems.
One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can create distinct and orthogonal vectors for each category, but it may increase the dimensionality and sparsity of the data.
NEW QUESTION # 43
Which two techniques are used to build personas in the ML development lifecycle? (Select two.)
- A. Population triage
- B. Population regression
- C. Population resampling
- D. Population variance
- E. Population estimates
Answer: A,E
Explanation:
Explanation
Personas are fictional characters that represent the potential users or customers of an ML system. Personas can help understand the needs, goals, preferences, and behaviors of the target audience, as well as design and evaluate the system from their perspective. Some of the techniques that are used to build personas in the ML development lifecycle are:
Population estimates: Population estimates are statistical methods that estimate the size, characteristics, and distribution of a population based on a sample or a census. Population estimates can help identify and quantify the potential market segments and user groups for an ML system, as well as their demographics, locations, and behaviors.
Population triage: Population triage is a process of prioritizing and selecting the most relevant and representative personas for an ML system based on some criteria or metrics. Population triage can help focus on the key user needs and scenarios, as well as avoid creating too many or too few personas.
NEW QUESTION # 44
Which of the following describes a benefit of machine learning for solving business problems?
- A. Improving the constraint of the problem
- B. Improving the quality of original data
- C. Increasing the speed of analysis
- D. Increasing the quantity of original data
Answer: C
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 # 45
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. Neural network model, because this is a classification problem with a large number of features.
- B. A decision tree algorithm, because the problem is a classification problem with a small number of features.
- C. Logistic regression, because this is a classification problem and our data is linearly separable.
- D. k-means, because this is a clustering problem with a small number of features.
Answer: A
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 # 46
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?
- A. The application was migrated from on-premise to a public cloud.
- B. The training data used was inaccurate.
- C. The team set flawed expectations when training the model.
- D. The AI model was trained in winter and applied in summer.
Answer: D
Explanation:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.
NEW QUESTION # 47
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. Information on the folder structure in your local machine
- B. Intermediate data files
- C. Link to a GitHub repository of the codebase
- D. README document
- E. Sample input and output data files
Answer: C,D,E
Explanation:
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 # 48
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 TF-IDF because in translation machines, we do not care about the order of the words.
- C. Using Word2vec because in translation machines, we do not care about the order of the words.
- D. Using Word2vec because in translation machines, we need to consider the order of the words.
Answer: D
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 # 49
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?
- A. Prevent hidden attacks
- B. Respond to hidden attacks
- C. Detect hidden attacks
- D. Recover from hidden attacks
Answer: A
Explanation:
Explanation
Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent hidden attacks, as it can help them detect any anomalies or deviations in the data or the model's performance that may indicate a potential attack.
NEW QUESTION # 50
Which of the following equations best represent an LI norm?
- A. |x|+|y|^2
- B. |x| + |y|
- C. |x|^2+|y|^2
- D. |x|-|y|
Answer: B
Explanation:
Explanation
An L1 norm is a measure of distance or magnitude that is defined as the sum of the absolute values of the components of a vector. For example, if x and y are two components of a vector, then the L1 norm of that vector is |x| + |y|. The L1 norm is also known as the Manhattan distance or the taxicab distance, as it represents the shortest path between two points in a grid-like city.
NEW QUESTION # 51
Which of the following best describes distributed artificial intelligence?
- A. It does not require hyperparemeter tuning because the distributed nature accounts for the bias.
- B. It uses a centralized system to speak to decentralized nodes.
- C. It relies on a distributed system that performs robust computations across a network of unreliable nodes.
- D. It intelligently pre-distributes the weight of starting a neural network.
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 # 52
Which of the following are true about the transform-design pattern for a machine learning pipeline? (Select three.) It aims to separate inputs from features.
- A. It encapsulates the processing steps of ML pipelines.
- B. It represents steps in the pipeline with a directed acyclic graph (DAG).
- C. It transforms the output data after production.
- D. It seeks to isolate individual steps of ML pipelines.
- E. It ensures reproducibility.
Answer: A,D,E
Explanation:
Explanation
The transform-design pattern for ML pipelines aims to separate inputs from features, encapsulate the processing steps of ML pipelines, and represent steps in the pipeline with a DAG. These goals help to make the pipeline modular, reusable, and easy to understand. The transform-design pattern does not seek to isolate individual steps of ML pipelines, as this would create entanglement and dependency issues. It also does not transform the output data after production, as this would violate the principle of separation of concerns.
NEW QUESTION # 53
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. High negative predictive value
- B. Low false positive rate
- C. Low false negative rate
- D. High positive predictive value
Answer: C
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 # 54
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. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
- D. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
Answer: B
Explanation:
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 # 55
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 validation occurs before model evaluation.
- C. Model evaluation and validation are the same.
- D. Model evaluation is defined as an external component.
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 # 56
You and your team need to process large datasets of images as fast as possible for a machine learning task.
The project will also use a modular framework with extensible code and an active developer community.
Which of the following would BEST meet your needs?
- A. Keras
- B. TensorBoard
- C. Microsoft Cognitive Services
- D. Caffe
Answer: D
Explanation:
Explanation
Caffe is a deep learning framework that is designed for speed and modularity. It can process large datasets of images efficiently and supports various types of neural networks. It also has a large and active developer community that contributes to its code base and documentation. Caffe is suitable for image processing tasks such as classification, segmentation, detection, and recognition
NEW QUESTION # 57
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
- A. Clustering
- B. Ridge regression
- C. Polynomial regression
- D. Linear regression
Answer: B
Explanation:
Explanation
Ridge regression is a type of regularization technique that can help reduce collinearity among independent variables. It does this by adding a penalty term to the ordinary least squares (OLS) objective function, which shrinks the coefficients of highly correlated variables towards zero. This reduces the variance of the coefficient estimates and improves the stability and accuracy of the regression model. References: Multicollinearity in Regression Analysis: Problems, Detection, and Solutions - Statistics By Jim, A Beginner's Guide to Collinearity: What it is and How it affects our regression model - StrataScratch
NEW QUESTION # 58
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?
- A. Stacking
- B. Weighted class
- C. Boosting
- D. Bagging
Answer: B
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 # 59
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?
- A. When the input volume changes
- B. As new resources become available
- C. Once a month
- 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 # 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. Ridge regression
- B. Polynomial regression
- C. Linear regression
- D. Lasso regression
Answer: C
NEW QUESTION # 61
Which of the following is TRUE about SVM models?
- A. They can be used only for classification.
- B. They use the sigmoid function to classify the data points.
- C. They can be used only for regression.
- D. They can take the feature space into higher dimensions to solve the problem.
Answer: D
Explanation:
Explanation
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively.
References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia
NEW QUESTION # 62
......
Pass CertNexus AIP-210 Premium Files Test Engine pdf - Free Dumps Collection: https://www.exam4tests.com/AIP-210-valid-braindumps.html
New 2023 Realistic AIP-210 Dumps Test Engine Exam Questions in here: https://drive.google.com/open?id=1kZY7Ws34uYO_uFTe9S6K9q-3H0Oi-ZE_