Verified AIP-210 exam dumps Q&As with Correct 92 Questions and Answers
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NEW QUESTION # 29
Which of the following is a type 1 error in statistical hypothesis testing?
- A. The null hypothesis is false, but fails to be rejected.
- B. The null hypothesis is true and fails to be rejected.
- C. The null hypothesis is false and is rejected.
- D. The null hypothesis is true, but is rejected.
Answer: D
Explanation:
Explanation
A type 1 error in statistical hypothesis testing is when the null hypothesis is true, but is rejected. This means that the test falsely concludes that there is a significant difference or effect when there is none. The probability of making a type 1 error is denoted by alpha, which is also known as the significance level of the test. A type 1 error can be reduced by choosing a smaller alpha value, but this may increase the chance of making a type 2 error, which is when the null hypothesis is false but fails to be rejected. References: [Type I and type II errors - Wikipedia], [Type I Error and Type II Error - Statistics How To]
NEW QUESTION # 30
Which of the following is NOT an activation function?
- A. ReLU
- B. Additive
- C. Sigmoid
- D. Hyperbolic tangent
Answer: B
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 # 31
Which of the following is TRUE about SVM models?
- A. They can take the feature space into higher dimensions to solve the problem.
- B. They can be used only for regression.
- C. They can be used only for classification.
- D. They use the sigmoid function to classify the data points.
Answer: A
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 # 32
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
- A. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
- B. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- C. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
- D. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
Answer: D
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 # 33
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 AI model was trained in winter and applied in summer.
- D. The team set flawed expectations when training the model.
Answer: C
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 # 34
Which of the following is the correct definition of the quality criteria that describes completeness?
- A. The degree to which the measures conform to defined business rules or constraints.
- B. The degree to which all required measures are known.
- C. The degree to which a set of measures are specified using the same units of measure in all systems.
- D. The degree to which a set of measures are equivalent across systems.
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 # 35
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. Data security
- B. Data privacy
- C. Cybersecurity
- D. Cyberprotection
Answer: B
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 # 36
Which two of the following decrease technical debt in ML systems? (Select two.)
- A. Design anti-patterns
- B. Refactoring
- C. Documentation readability
- D. Boundary erosion
- E. Model complexity
Answer: B,C
Explanation:
Explanation
Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to various factors, such as changing requirements, outdated code, poor documentation, or lack of testing. Some of the ways to decrease technical debt in ML systems are:
Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent information about the system's design, functionality, performance, and maintenance. Documentation readability can also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and managers.
Refactoring: Refactoring is the process of improving the structure and quality of code without changing its functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity, or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.
NEW QUESTION # 37
An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?
- A. When concept drift is detected
- B. Periodically every year
- C. When data drift is detected
- D. Periodically before New Year's Day and after New Year's Day
Answer: B
Explanation:
Explanation
Retraining is the process of updating an existing ML model with new or updated data to maintain or improve its performance and relevance. Retraining can help address various issues or challenges in ML systems, such as data drift, concept drift, model degradation, or changing requirements. Retraining can be done using different strategies, such as periodically, continuously, or on-demand.
For an AI system that recommends New Year's resolutions, retraining periodically every year would be the best strategy for this pipeline. This is because New Year's resolutions are seasonal and time-sensitive, meaning that they may vary depending on the year or the current situation. Retraining periodically every year can help ensure that the system's recommendations are up-to-date and relevant for each new year.
NEW QUESTION # 38
An AI practitioner incorporates risk considerations into a deployment plan and decides to log and store historical predictions for potential, future access requests.
Which ethical principle is this an example of?
- A. Privacy
- B. Safety
- C. Fairness
- D. Transparency
Answer: D
Explanation:
Explanation
Transparency is an ethical principle that describes the degree to which an AI system can provide clear and understandable information about its inputs, outputs, processes, and decisions. Transparency can help increase trust and confidence among users and stakeholders, as well as enable accountability and responsibility for the system's actions and outcomes. Logging and storing historical predictions for potential, future access requests is an example of transparency, as it can help provide evidence and explanation for the system's recommendations, as well as facilitate auditing and feedback.
NEW QUESTION # 39
Which of the following scenarios is an example of entanglement in ML pipelines?
- A. Change the way output is visualized in the monitoring step.
- B. Change in normalization function in the feature engineering step.
- C. Add a new pipeline for retraining the model in the model training step.
- D. Add a new method for drift detection in the model evaluation step.
Answer: B
Explanation:
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 # 40
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 # 41
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 less robust against adversarial attacks.
- D. More accurately are neither more nor less robust against adversarial attacks.
Answer: C
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 # 42
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Portability
- B. Scalability
- C. Data size
- D. Explainability
- E. Complexity
Answer: B,D
Explanation:
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 # 43
What is the open framework designed to help detect, respond to, and remediate threats in ML systems?
- A. MITRE ATT&CK Matrix
- B. Adversarial ML Threat Matrix
- C. Threat Susceptibility Matrix
- D. OWASP Threat and Safeguard Matrix
Answer: B
Explanation:
Explanation
The Adversarial ML Threat Matrix is an open framework designed to help detect, respond to, and remediate threats in ML systems. The Adversarial ML Threat Matrix is inspired by the MITRE ATT&CK Matrix1, which is a framework for describing cyberattacks across various stages of an attack lifecycle. The Adversarial ML Threat Matrix adapts this framework to address specific threats and vulnerabilities in ML systems, such as data poisoning, model stealing, model evasion, or model inversion2. The Adversarial ML Threat Matrix provides a structured way to organize and classify adversarial techniques, tactics, procedures, examples, and mitigations for ML systems2.
NEW QUESTION # 44
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. Increasing the speed of analysis
- D. Improving the quality 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
Which of the following models are text vectorization methods? (Select two.)
- A. PCA
- B. Lemmatization
- C. Tokenization
- D. TF-IDF
- E. Skip-gram
- F. t-SNE
Answer: D,E
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 # 46
A dataset can contain a range of values that depict a certain characteristic, such as grades on tests in a class during the semester. A specific student has so far received the following grades: 76,81, 78, 87, 75, and 72.
There is one final test in the semester. What minimum grade would the student need to achieve on the last test to get an 80% average?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
Explanation:
Explanation
To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:
minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1) Plugging in the given values, we get:
minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)
minimum grade = (560 - 469) / 1
minimum grade = 91
Therefore, the student needs to score at least 91 on the last test to get an 80% average.
NEW QUESTION # 47
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. k-means, because this is a clustering problem with a small number of features.
- C. A decision tree algorithm, because the problem is a classification problem with a small number of features.
- D. Logistic regression, because this is a classification problem and our data is linearly separable.
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 # 48
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. Sample input and output data files
- C. README document
- D. Intermediate data files
- E. Link to a GitHub repository of the codebase
Answer: B,C,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 # 49
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?
- A. Reduction in latency
- B. Guaranteed availability of enough space
- C. A more complex model
- D. Increase in data bandwidth consumption
Answer: A
Explanation:
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 # 50
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