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NEW QUESTION # 27
The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?
- A. 48/(48+37)
- B. 37/(37+8)
- C. 37/(37+7)
- D. (48+37)/100
Answer: B
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 # 28
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. Log Encoder
- B. Count Encoder
- C. Mean Encoder
- D. One-Hot Encoder
- E. Median 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 # 29
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. Destroy
- B. Duplicate
- C. De-Duplicate
- D. Detain
Answer: A
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 # 30
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. Dijkstra Algorithm
- B. Supervised Learning.
- C. Reinforcement learning
- D. Unsupervised Learning
Answer: C
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 # 31
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 # 32
Which of the following principles supports building an ML system with a Privacy by Design methodology?
- A. Avoiding mechanisms to explain and justify automated decisions.
- B. Collecting and processing the largest amount of data possible.
- C. Understanding, documenting, and displaying data lineage.
- D. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
Answer: C
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 # 33
When should you use semi-supervised learning? (Select two.)
- A. A small set of labeled data is available but not representative of the entire distribution.
- B. There is a large amount of labeled data to be used for predictions.
- C. A small set of labeled data is biased toward one class.
- D. Labeling data is challenging and expensive.
- E. There is a large amount of unlabeled data to be used for predictions.
Answer: D,E
Explanation:
Explanation
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can be useful when:
Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. 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.
There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. 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 # 34
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. Delete entire rows that contain any missing features.
- C. Delete entire columns that contain any missing features.
- D. Fill in missing features with random values for that feature in the training set.
Answer: A
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 # 35
What is the open framework designed to help detect, respond to, and remediate threats in ML systems?
- A. OWASP Threat and Safeguard Matrix
- B. MITRE ATT&CK Matrix
- C. Adversarial ML Threat Matrix
- D. Threat Susceptibility Matrix
Answer: C
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 # 36
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. ISO/IEC 27001
- B. General Data Protection Regulation (GDPR)
- C. Sarbanes Oxley (SOX)
- D. PCIDSS
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 # 37
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?
- A. Detect hidden attacks
- B. Recover from hidden attacks
- C. Prevent hidden attacks
- D. Respond to hidden attacks
Answer: C
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 # 38
Which of the following is the correct definition of the quality criteria that describes completeness?
- A. The degree to which a set of measures are specified using the same units of measure in all systems.
- B. The degree to which a set of measures are equivalent across systems.
- C. The degree to which all required measures are known.
- D. The degree to which the measures conform to defined business rules or constraints.
Answer: C
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 # 39
Which of the following is the definition of accuracy?
- A. (True Positives + True Negatives) / Total Predictions
- B. True Positives / (True Positives + False Negatives)
- C. True Positives / (True Positives + False Positives)
- D. (True Positives + False Positives) / Total Predictions
Answer: A
Explanation:
Explanation
Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).
NEW QUESTION # 40
Which of the following is NOT a valid cross-validation method?
- A. K-fold
- B. Bootstrapping
- C. Leave-one-out
- D. Stratification
Answer: D
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 # 41
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. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
- C. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
- D. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
Answer: C
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 # 42
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?
- A. Code Repository
- B. Incident
- C. Configuration Management
- D. Asset
Answer: B
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 # 43
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. Change in normalization function in the feature engineering step.
- C. Change the way output is visualized in the monitoring 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 # 44
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. Standardization
- B. Log transformation
- C. Normalization
- D. Square-root transformation
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 # 45
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
- A. Linear regression
- B. Ridge regression
- C. Polynomial regression
- D. Clustering
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 # 46
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. Precision and recall
- B. Precision and accuracy
- C. Recall and explained variance
- D. Mean squared error
Answer: A
Explanation:
Explanation
Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).
NEW QUESTION # 47
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?
- A. Guaranteed availability of enough space
- B. A more complex model
- C. Reduction in latency
- D. Increase in data bandwidth consumption
Answer: C
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 # 48
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