92 Q&As in UPDATED AIP-210 Exam Questions Certification Test Engine to PDF
Get The Important Preparation Guide With AIP-210 Dumps
CertNexus AIP-210 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
NEW QUESTION # 31
Which of the following approaches is best if a limited portion of your training data is labeled?
- A. Dimensionality reduction
- B. Reinforcement learning
- C. Probabilistic clustering
- D. Semi-supervised learning
Answer: D
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 # 32
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
- A. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
- B. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- C. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
- D. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
Answer: A
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
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. Increase in data bandwidth consumption
- D. A more complex model
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 # 34
Which of the following models are text vectorization methods? (Select two.)
- A. Lemmatization
- B. t-SNE
- C. PCA
- D. Tokenization
- E. Skip-gram
- F. TF-IDF
Answer: E,F
Explanation:
Explanation
Skip-gram and TF-IDF are both text vectorization methods that convert text into numerical feature vectors.
Skip-gram is a prediction-based word embedding method that learns vector representations of words from their contexts in a large corpus of text. TF-IDF is a frequency-based word weighting method that assigns scores to words based on their importance in a document and in a corpus of documents. References: Text Vectorization and Word Embedding | Guide to Master NLP (Part 5), What Is Text Vectorization? Everything You Need to Know - deepset
NEW QUESTION # 35
Which of the following is NOT an activation function?
- A. Hyperbolic tangent
- B. Additive
- C. Sigmoid
- D. ReLU
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 # 36
Which of the following unsupervised learning models can a bank use for fraud detection?
- A. k-means
- B. Hierarchical clustering
- C. Anomaly detection
- D. DB5CAN
Answer: C
Explanation:
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 # 37
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 team set flawed expectations when training the model.
- B. The training data used was inaccurate.
- C. The AI model was trained in winter and applied in summer.
- D. The application was migrated from on-premise to a public cloud.
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 # 38
Which of the following statements are true regarding highly interpretable models? (Select two.)
- A. They are usually binary classifiers.
- B. They are usually referred to as "black box" models.
- C. They usually compromise on model accuracy for the sake of interpretability.
- D. They are usually easier to explain to business stakeholders.
- E. They are usually very good at solving non-linear problems.
Answer: C,D
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 # 39
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. Principal components analysis
- B. Normalization
- C. Oversampling
- D. Quality filtering
Answer: C
Explanation:
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 # 40
Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)
- A. If it was requested by the authority of law.
- B. If it was collected accidentally.
- C. If it is for a good cause.
- D. If the data is only collected once.
- E. If it was with consent of the person it is collected from.
Answer: A,E
Explanation:
Explanation
Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.
NEW QUESTION # 41
A big data architect needs to be cautious about personally identifiable information (PII) that may be captured with their new IoT system. What is the final stage of the Data Management Life Cycle, which the architect must complete in order to implement data privacy and security appropriately?
- A. Duplicate
- B. Destroy
- C. De-Duplicate
- D. Detain
Answer: B
Explanation:
Explanation
The final stage of the data management life cycle is data destruction, which is the process of securely deleting or erasing data that is no longer needed or relevant for the organization. Data destruction ensures that data is disposed of in compliance with any legal or regulatory requirements, as well as any internal policies or standards. Data destruction also protects the organization from potential data breaches, leaks, or thefts that could compromise its privacy and security. Data destruction can be performed using various methods, such as overwriting, degaussing, shredding, or incinerating
NEW QUESTION # 42
A product manager is designing an Artificial Intelligence (AI) solution and wants to do so responsibly, evaluating both positive and negative outcomes.
The team creates a shared taxonomy of potential negative impacts and conducts an assessment along vectors such as severity, impact, frequency, and likelihood.
Which modeling technique does this team use?
- A. Harms
- B. Business
- C. Process
- D. Threat
Answer: A
Explanation:
Explanation
Harms modeling is a technique that helps product managers design AI solutions responsibly by evaluating both positive and negative outcomes. Harms modeling involves creating a shared taxonomy of potential negative impacts and conducting an assessment along vectors such as severity, impact, frequency, and likelihood. Harms modeling can help identify and mitigate any risks or harms that may arise from using AI solutions. References: [Harms Modeling for Responsible AI | by Google Developers | Google Developers],
[Harms Modeling for Responsible AI - YouTube]
NEW QUESTION # 43
What is Word2vec?
- A. A matrix of how frequently words appear in a group of documents.
- B. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
- C. A bag of words.
- D. A word embedding method that finds characteristics of words in a very large number of documents.
Answer: D
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 # 44
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 random values for that feature in the training set.
- B. Fill in missing features with the average of observed values for that feature in the entire dataset.
- C. Delete entire columns that contain any missing features.
- D. Delete entire rows that contain any missing features.
Answer: B
Explanation:
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 # 45
Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?
- A. Stereotype bias
- B. Sampling bias
- C. Algorithmic bias
- D. Systematic value distortion
Answer: B
Explanation:
Explanation
Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]
NEW QUESTION # 46
Which of the following describes a benefit of machine learning for solving business problems?
- A. Increasing the quantity of original data
- B. Improving the quality of original data
- C. Increasing the speed of analysis
- D. Improving the constraint of the problem
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 # 47
......
Prepare With Top Rated High-quality AIP-210 Dumps For Success in Exam: https://prep4sure.real4dumps.com/AIP-210-prep4sure-exam.html
