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NEW QUESTION # 14
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 transforms the output data after production.
- B. It seeks to isolate individual steps of ML pipelines.
- C. It encapsulates the processing steps of ML pipelines.
- D. It represents steps in the pipeline with a directed acyclic graph (DAG).
- E. It ensures reproducibility.
Answer: B,C,E
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 # 15
You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?
- A. When it is necessary to save computational time.
- B. When there is high correlation among the features.
- C. When the categories of the dependent variable are not linearly separable.
- D. When the distribution of the dependent variable is Gaussian.
Answer: C
Explanation:
Explanation
A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.
NEW QUESTION # 16
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. Mean squared error
- C. Precision and accuracy
- D. Recall and explained variance
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 # 17
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 Word2vec because in translation machines, we do not care about the order of the words.
- C. Using TF-IDF 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 # 18
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 pipeline for retraining the model in the model training step.
- D. Add a new method for drift detection in the model evaluation 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 # 19
Which of the following unsupervised learning models can a bank use for fraud detection?
- A. k-means
- B. Anomaly detection
- C. DB5CAN
- D. Hierarchical clustering
Answer: B
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 # 20
What is the primary benefit of the Federated Learning approach to machine learning?
- A. It does not require a labeled dataset to solve supervised learning problems.
- B. It uses large, centralized data stores to train complex machine learning models.
- C. It protects the privacy of the user's data while providing well-trained models.
- D. It requires less computation to train the same model using a traditional approach.
Answer: C
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 # 21
Why do data skews happen in the ML pipeline?
- A. Test and evaluation data are designed incorrectly.
- B. There is insufficient training data for evaluation.
- C. There Is a mismatch between live input data and offline data.
- D. There is a mismatch between live output data and offline data.
Answer: C
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 # 22
Which of the following best describes distributed artificial intelligence?
- A. It relies on a distributed system that performs robust computations across a network of unreliable nodes.
- B. It uses a centralized system to speak to decentralized nodes.
- C. It intelligently pre-distributes the weight of starting a neural network.
- D. It does not require hyperparemeter tuning because the distributed nature accounts for the bias.
Answer: A
Explanation:
Explanation
Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that studies how multiple intelligent agents can coordinate and cooperate to achieve a common goal or solve a complex problem. DAI relies on a distributed system that performs robust computations across a network of unreliable nodes, such as sensors, robots, or humans. DAI can handle large-scale, dynamic, and uncertain environments that are beyond the capabilities of a single agent. References: [Distributed artificial intelligence - Wikipedia], [Distributed Artificial Intelligence: An Overview]
NEW QUESTION # 23
Word Embedding describes a task in natural language processing (NLP) where:
- A. Words are featurized by taking a histogram of letter counts.
- B. Words are converted into numerical vectors.
- C. Words are featurized by taking a matrix of bigram counts.
- D. Words are grouped together into clusters and then represented by word cluster membership.
Answer: B
Explanation:
Explanation
Word embedding is a task in natural language processing (NLP) where words are converted into numerical vectors that represent their meaning, usage, or context. Word embedding can help reduce the dimensionality and sparsity of text data, as well as enable various operations and comparisons among words based on their vector representations. Some of the common methods for word embedding are:
One-hot encoding: One-hot encoding is a method that assigns a unique binary vector to each word in a vocabulary. The vector has only one element with a value of 1 (the hot bit) and the rest with a value of
0. One-hot encoding can create distinct and orthogonal vectors for each word, but it does not capture any semantic or syntactic information about words.
Word2vec: Word2vec is a method that 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 associations.
GloVe: GloVe (Global Vectors for Word Representation) is a method that combines the advantages of count-based methods (such as TF-IDF) and predictive methods (such as Word2vec) to create word vectors. GloVe can leverage both global and local information from a large corpus of text to capture the co-occurrence patterns and probabilities of words.
NEW QUESTION # 24
Which of the following tests should be performed at the production level before deploying a newly retrained model?
- A. Performance test
- B. A/Btest
- C. Unit test
- D. Security test
Answer: A
Explanation:
Explanation
Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. References: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]
NEW QUESTION # 25
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?
- A. When the input volume changes
- B. Once a month
- C. As new resources become available
- D. When the input format changes
Answer: D
Explanation:
Explanation
The input format is the way that the data is structured, organized, and presented to the model. For example, the input format could be a CSV file, an image file, or a JSON object. The input format can affect how the model interprets and processes the data, and therefore how it makes predictions. When the input format changes, it may require retraining the model to adapt to the new format and ensure its accuracy and reliability. For example, if the weather prediction application switches from using numerical values to categorical values for some features, such as wind direction or cloud cover, it may need to retrain the model to handle these changes
.
NEW QUESTION # 26
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?
- A. Bagging
- B. Weighted class
- C. Stacking
- D. Boosting
Answer: B
Explanation:
Explanation
Weighted class is a technique to rebalance a dataset by assigning different weights to each class, according to their frequency in the dataset. The weights are inversely proportional to the class frequency, meaning that rare classes have higher weights and common classes have lower weights. This helps to reduce the bias towards the majority class and improve the model performance on the minority class. References: 4. Data Validation - Building Machine Learning Pipelines, A guide to React design patterns - LogRocket Blog
NEW QUESTION # 27 
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 # 28
A change in the relationship between the target variable and input features is
- A. data drift.
- B. model decay.
- C. concept drift.
- D. covariate shift.
Answer: C
Explanation:
Explanation
Concept drift, also known as model drift, occurs when the task that the model was designed to perform changes over time. For example, imagine that a machine learning model was trained to detect spam emails based on the content of the email. If the types of spam emails that people receive change significantly, the model may no longer be able to accurately detect spam. References: Understanding Data Drift and Model Drift: Drift Detection in Python | DataCamp, Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift
NEW QUESTION # 29
In general, models that perform their tasks:
- A. More accurately are neither more nor less robust against adversarial attacks.
- B. Less accurately are neither more nor less robust against adversarial attacks.
- C. Less accurately are less robust against adversarial attacks.
- D. More accurately are less robust against adversarial attacks.
Answer: D
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 # 30
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Complexity
- B. Scalability
- C. Portability
- D. Data size
- E. Explainability
Answer: B,E
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 # 31
Which of the following is the definition of accuracy?
- A. (True Positives + False Positives) / Total Predictions
- B. (True Positives + True Negatives) / Total Predictions
- C. True Positives / (True Positives + False Positives)
- D. True Positives / (True Positives + False Negatives)
Answer: B
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 # 32
For a particular classification problem, you are tasked with determining the best algorithm among SVM, random forest, K-nearest neighbors, and a deep neural network. Each of the algorithms has similar accuracy on your data. The stakeholders indicate that they need a model that can convey each feature's relative contribution to the model's accuracy. Which is the best algorithm for this use case?
- A. K-nearest neighbors
- B. Random forest
- C. Deep neural network
- D. SVM
Answer: B
Explanation:
Explanation
Random forest is an ensemble learning method that combines multiple decision trees to create a more accurate and robust classifier or regressor. Random forest can convey each feature's relative contribution to the model's accuracy by measuring how much the prediction error increases when a feature is randomly permuted. This metric is called feature importance or Gini importance. Random forest can also provide insights into the interactions and dependencies among features by visualizing the decision trees .
NEW QUESTION # 33
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. Use Secrets Manager to protect credentials.
- C. Use data encryption.
- D. Use max privilege to control access to ML artifacts.
- E. Disable logging for model access.
- F. Monitor model degradation.
Answer: A,B,C
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 # 34
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