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NEW QUESTION # 22
You train a neural network model with two layers, each layer having four nodes, and realize that the model is underfit. Which of the actions below will NOT work to fix this underfitting?
- A. Increase the complexity of the model
- B. Add features to training data
- C. Get more training data
- D. Train the model for more epochs
Answer: C
Explanation:
Explanation
Underfitting is a problem that occurs when a model learns too little from the training data and fails to capture the underlying complexity or structure of the data. Underfitting can result from using insufficient or irrelevant features, a low complexity of the model, or a lack of training data. Underfitting can reduce the accuracy and generalization of the model, as it may produce oversimplified or inaccurate predictions. Some of the ways to fix underfitting are:
Add features to training data: Adding more features or variables to the training data can help increase the information and diversity of the data, which can help the model learn more complex patterns and relationships.
Increase the complexity of the model: Increasing the complexity of the model can help increase its expressive power and flexibility, which can help it fit better to the data. For example, adding more layers or nodes to a neural network can increase its complexity.
Train the model for more epochs: Training the model for more epochs can help increase its learning ability and convergence, which can help it optimize its parameters and reduce its error.
Getting more training data will not work to fix underfitting, as it will not change the complexity or structure of the data or the model. Getting more training data may help with overfitting, which is when a model learns too much from the training data and fails to generalize well to new or unseen data.
NEW QUESTION # 23
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. Sarbanes Oxley (SOX)
- B. PCIDSS
- C. ISO/IEC 27001
- D. General Data Protection Regulation (GDPR)
Answer: D
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 # 24
In general, models that perform their tasks:
- A. More 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. Less 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 # 25
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. Unsupervised Learning
- B. Reinforcement learning
- C. Dijkstra Algorithm
- D. Supervised Learning.
Answer: B
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 # 26
Which of the following approaches is best if a limited portion of your training data is labeled?
- A. Probabilistic clustering
- B. Semi-supervised learning
- C. Dimensionality reduction
- D. Reinforcement learning
Answer: B
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 # 27
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
- A. Model validation occurs before model evaluation.
- B. Model validation is defined as a set of tasks to confirm the model performs as expected.
- C. Model evaluation and validation are the same.
- D. Model evaluation is defined as an external component.
Answer: B
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 # 28
Below are three tables: Employees, Departments, and Directors.
Employee_Table
Department_Table
Director_Table
ID
Firstname
Lastname
Age
Salary
DeptJD
4566
Joey
Morin
62
$ 122,000
1
1230
Sam
Clarck
43
$ 95,670
2
9077
Lola
Russell
54
$ 165,700
3
1346
Lily
Cotton
46
$ 156,000
4
2088
Beckett
Good
52
$ 165,000
5
Which SQL query provides the Directors' Firstname, Lastname, the name of their departments, and the average employee's salary?
- A. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_Salary FROM Employee_Table as e RIGHT JOIN Department_Table as d on e.Dept = d.Name INNER JOIN Directorjable as m on d.ID = m.DeptJD GROUP BY e.Salary
- B. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_Salary FROM Employee_Table as e RIGHT JOIN Departmentjable as d on e.Dept = d.Name INNER JOIN Directorjable as m on d.ID = m.DeptJD GROUP BY d.Name
- C. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Saiary) as Dept_avg_Saiary FROM Employee_Table as e LEFT JOIN Department_Table as d on e.Dept = d.Name LEFT JOIN Directorjable as m on d.ID = m.DeptJD GROUP BY m.Firstname, m.Lastname, d.Name
- D. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_Salary FROM Employee_Table as e RIGHT JOIN Department_Table as d on e.Dept = d.Name INNER JOIN Directorjable as m on d.ID = m.DeptID GROUP BY m.Firstname, m.Lastname, d.Name
Answer: D
Explanation:
Explanation
This SQL query provides the Directors' Firstname, Lastname, the name of their departments, and the average employee's salary by joining the three tables using the appropriate join types and conditions. The RIGHT JOIN between Employee_Table and Department_Table ensures that all departments are included in the result, even if they have no employees. The INNER JOIN between Department_Table and Directorjable ensures that only departments with directors are included in the result. The GROUP BY clause groups the result by the directors' names and departments' names, and calculates the average salary for each group using the AVG function. References: SQL Joins - W3Schools, SQL GROUP BY Statement - W3Schools
NEW QUESTION # 29
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 ensures reproducibility.
- B. It seeks to isolate individual steps of ML pipelines.
- C. It represents steps in the pipeline with a directed acyclic graph (DAG).
- D. It encapsulates the processing steps of ML pipelines.
- E. It transforms the output data after production.
Answer: A,B,D
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 # 30
Why do data skews happen in the ML pipeline?
- A. Test and evaluation data are designed incorrectly.
- B. There Is a mismatch between live input data and offline data.
- C. There is a mismatch between live output data and offline data.
- D. There is insufficient training data for evaluation.
Answer: B
Explanation:
Explanation
Data skews happen in the ML pipeline when the distribution or characteristics of the live input data differ from those of the offline data used for training and testing the model. This can lead to a degradation of the model performance and accuracy, as the model is not able to generalize well to new data. Data skews can be caused by various factors, such as changes in user behavior, data collection methods, data quality issues, or external events. References: What is training-serving skew in Machine Learning?, Data preprocessing for ML: options and recommendations
NEW QUESTION # 31
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. README document
- B. Link to a GitHub repository of the codebase
- C. Information on the folder structure in your local machine
- D. Sample input and output data files
- E. Intermediate data files
Answer: A,B,D
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 # 32
Which of the following algorithms is an example of unsupervised learning?
- A. Principal components analysis
- B. Random forest
- C. Ridge regression
- D. Neural networks
Answer: A
Explanation:
Explanation
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining. Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.
NEW QUESTION # 33
What is the open framework designed to help detect, respond to, and remediate threats in ML systems?
- A. OWASP Threat and Safeguard Matrix
- B. Adversarial ML Threat Matrix
- C. Threat Susceptibility Matrix
- D. MITRE ATT&CK 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 # 34
Which of the following scenarios is an example of entanglement in ML pipelines?
- A. Change in normalization function in the feature engineering step.
- B. Change the way output is visualized in the monitoring step.
- C. Add a new method for drift detection in the model evaluation step.
- D. Add a new pipeline for retraining the model in the model training step.
Answer: A
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 # 35
What is Word2vec?
- A. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
- B. A bag of words.
- C. A word embedding method that finds characteristics of words in a very large number of documents.
- D. A matrix of how frequently words appear in a group of documents.
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 # 36
R-squared is a statistical measure that:
- A. Represents the extent to which two random variables vary together.
- B. Expresses the extent to which two variables are linearly related.
- C. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
- D. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
Answer: D
Explanation:
Explanation
R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by dividing the explained variance by the total variance. The explained variance is the amount of variation in the dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1 means perfect fit.
NEW QUESTION # 37
You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?
- A. Decision tree
- B. Logistic regression
- C. Random forest
- D. XGBoost
Answer: C
Explanation:
Explanation
Random forest is an algorithm that is ideal to prevent overfitting when using a dataset with many features and a small sample size. Random forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model. Random forest can prevent overfitting by introducing randomness and diversity into the model, such as by using bootstrap sampling (sampling with replacement) to create different subsets of data for each tree, or by using feature selection (choosing a random subset of features) to split each node in a tree.
NEW QUESTION # 38
Which of the following is the definition of accuracy?
- A. (True Positives + False Positives) / Total Predictions
- B. True Positives / (True Positives + False Negatives)
- C. (True Positives + True Negatives) / Total Predictions
- D. True Positives / (True Positives + False Positives)
Answer: C
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 # 39
In which of the following scenarios is lasso regression preferable over ridge regression?
- A. There are many features with no association with the dependent variable.
- B. The number of features is much larger than the sample size.
- C. There is high collinearity among some of the features associated with the dependent variable.
- D. The sample size is much larger than the number of features.
Answer: A
Explanation:
Explanation
Lasso regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of the coefficients to zero, which effectively eliminates some of the features from the model. Lasso regression is preferable over ridge regression when there are many features with no association with the dependent variable, as it can perform feature selection and reduce the complexity and noise of the model.
NEW QUESTION # 40
Which of the following describes a typical use case of video tracking?
- A. Medical diagnosis
- B. Augmented dreaming
- C. Traffic monitoring
- D. Video composition
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 # 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. Detain
- B. De-Duplicate
- C. Destroy
- D. Duplicate
Answer: C
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 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 # 43
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