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NEW QUESTION # 35
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. PCIDSS
- B. General Data Protection Regulation (GDPR)
- C. ISO/IEC 27001
- D. Sarbanes Oxley (SOX)
Answer: B
Explanation:
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 # 36
Which of the following is the primary purpose of hyperparameter optimization?
- A. Improves model interpretability
- B. Increases recall over precision
- C. Controls the learning process of a given algorithm
- D. Makes models easier to explain to business stakeholders
Answer: C
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 # 37
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:
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 # 38
Which of the following describes a typical use case of video tracking?
- A. Video composition
- B. Augmented dreaming
- C. Traffic monitoring
- D. Medical diagnosis
Answer: C
Explanation:
Explanation
Video tracking is a technique that involves detecting and following moving objects in a video sequence. Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human-computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.
NEW QUESTION # 39
Which of the following is NOT a valid cross-validation method?
- A. Bootstrapping
- B. Leave-one-out
- C. Stratification
- D. K-fold
Answer: C
Explanation:
Explanation
Stratification is not a valid cross-validation method, but a technique to ensure that each subset of data has the same proportion of classes or labels as the original data. Stratification can be used in conjunction with cross-validation methods such as k-fold or leave-one-out to preserve the class distribution and reduce bias or variance in the validation results. Bootstrapping, k-fold, and leave-one-out are all valid cross-validation methods that use different ways of splitting and resampling the data to estimate the performance of a machine learning model.
NEW QUESTION # 40
Which three security measures could be applied in different ML workflow stages to defend them against malicious activities? (Select three.)
- A. Launch ML Instances In a virtual private cloud (VPC).
- B. Disable logging for model access.
- C. Use Secrets Manager to protect credentials.
- D. Monitor model degradation.
- E. Use data encryption.
- F. Use max privilege to control access to ML artifacts.
Answer: A,C,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 # 41
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?
- A. Respond to hidden attacks
- B. Prevent hidden attacks
- C. Detect hidden attacks
- D. Recover from hidden attacks
Answer: B
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 # 42
What is Word2vec?
- A. A bag of words.
- B. A matrix of how frequently words appear in a group of documents.
- C. A word embedding method that finds characteristics of words in a very large number of documents.
- D. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
Answer: C
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 # 43
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. Sample input and output data files
- B. Link to a GitHub repository of the codebase
- C. Information on the folder structure in your local machine
- D. Intermediate data files
- E. README document
Answer: A,B,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 # 44
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?
- A. Configuration Management
- B. Asset
- C. Code Repository
- D. Incident
Answer: D
Explanation:
Explanation
An incident database is a database that is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems. An incident database collects and stores information about incidents or events where AI systems have caused or contributed to negative outcomes or harms, such as accidents, errors, biases, discriminations, or violations. An incident database can help identify patterns, trends, causes, impacts, and solutions for AI-related incidents, as well as provide guidance and best practices for preventing or mitigating future incidents.
NEW QUESTION # 45
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. Log Encoder
- B. Median Encoder
- C. Mean Encoder
- D. One-Hot Encoder
- E. Count Encoder
Answer: C,D
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 # 46
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. Detain
- B. Duplicate
- C. De-Duplicate
- D. Destroy
Answer: D
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 # 47
Which of the following principles supports building an ML system with a Privacy by Design methodology?
- A. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
- B. Avoiding mechanisms to explain and justify automated decisions.
- C. Collecting and processing the largest amount of data possible.
- D. Understanding, documenting, and displaying data lineage.
Answer: D
Explanation:
Explanation
Data lineage is the process of tracking the origin, transformation, and usage of data throughout its lifecycle. It helps to ensure data quality, integrity, and provenance. Data lineage also supports the Privacy by Design methodology, which is a framework that aims to embed privacy principles into the design and operation of systems, processes, and products that involve personal data. By understanding, documenting, and displaying data lineage, an ML system can demonstrate how it collects, processes, stores, and deletes personal data in a transparent and accountable manner3 .
NEW QUESTION # 48
The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?
- A. 37/(37+8)
- B. (48+37)/100
- C. 48/(48+37)
- D. 37/(37+7)
Answer: A
Explanation:
Explanation
Precision is a measure of how well a classifier can avoid false positives (incorrectly predicted positive cases).
Precision is calculated by dividing the number of true positives (correctly predicted positive cases) by the number of predicted positive cases (true positives and false positives). In this confusion matrix, the true positives are 37 and the false positives are 8, so the precision is 37/(37+8) = 0.822.
NEW QUESTION # 49
Which of the following is TRUE about SVM models?
- A. They can be used only for classification.
- B. They can be used only for regression.
- C. They can take the feature space into higher dimensions to solve the problem.
- D. They use the sigmoid function to classify the data points.
Answer: C
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 # 50
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. OWASP Threat and Safeguard Matrix
- D. Threat Susceptibility 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 # 51 
The graph is an elbow plot showing the inertia or within-cluster sum of squares on the y-axis and number of clusters (also called K) on the x-axis, denoting the change in inertia as the clusters change using k-means algorithm.
What would be an optimal value of K to ensure a good number of clusters?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
Explanation:
Explanation
The optimal value of K is the one that minimizes the inertia or within-cluster sum of squares, while avoiding too many clusters that may overfit the data. The elbow plot shows a sharp decrease in inertia from K = 1 to K
= 2, and then a more gradual decrease from K = 2 to K = 3. After K = 3, the inertia does not change much as K increases. Therefore, the elbow point is at K = 3, which is the optimal value of K for this data. References:
How to Run K-Means Clustering in Python, K-means clustering - Wikipedia
NEW QUESTION # 52
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 before New Year's Day and after New Year's Day
- C. Periodically every year
- D. When data drift is detected
Answer: C
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 # 53
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 linear regression
- D. A form of a quantile regression
Answer: C
Explanation:
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 # 54
Which of the following is a type 1 error in statistical hypothesis testing?
- A. The null hypothesis is false and is rejected.
- B. The null hypothesis is false, but fails to be rejected.
- C. The null hypothesis is true and fails to be 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 # 55
What is the primary benefit of the Federated Learning approach to machine learning?
- A. It protects the privacy of the user's data while providing well-trained models.
- B. It does not require a labeled dataset to solve supervised learning problems.
- C. It uses large, centralized data stores to train complex machine learning models.
- D. It requires less computation to train the same model using a traditional approach.
Answer: A
Explanation:
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 # 56
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