May-2024 New Version AIP-210 Certificate & Helpful Exam Dumps is Online
AIP-210 Free Certification Exam Material with 92 Q&As
CertNexus AIP-210 Exam Syllabus Topics:
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NEW QUESTION # 23
Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression.
What should you do before log-transforming Y?
- A. Subtract the mean of Y from all the Y values.
- B. Add 1 to all of the Y values.
- C. Explore the data for outliers.
- D. Divide all the Y values by the standard deviation of Y.
Answer: B
Explanation:
Explanation
Before log-transforming Y, we should add 1 to all of the Y values. This is because log transformation is undefined for zero or negative values, and some of the Y values may be zero. Adding 1 to all of the Y values can avoid this problem and ensure that the log transformation is valid and meaningful. Adding 1 to all of the Y values is also known as a log-plus-one transformation.
NEW QUESTION # 24
Which of the following algorithms is an example of unsupervised learning?
- A. Ridge regression
- B. Random forest
- C. Neural networks
- D. Principal components analysis
Answer: D
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 # 25
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A. Bag of bigrams (2 letter pairs)
- B. Word2Vec algorithm
- C. Bag of words model with TF-IDF
- D. Clustering similar words and representing words by group membership
Answer: A
Explanation:
Explanation
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word "hello" would be represented as
{"he": 1, "el": 1, "ll": 1, "lo": 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as "th" in English or "ch" in German .
NEW QUESTION # 26
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?
- A. When the input format changes
- B. Once a month
- C. When the input volume changes
- D. As new resources become available
Answer: A
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 # 27
Which of the following text vectorization methods is appropriate and correctly defined for an English-to-Spanish translation machine?
- A. Using Word2vec because in translation machines, we need to consider the order of the words.
- B. Using TF-IDF because in translation machines, we do not care about the order of the words.
- C. Using Word2vec because in translation machines, we do not care about the order of the words.
- D. Using TF-IDF because in translation machines, we need to consider the order of the words.
Answer: A
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 # 28
You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive treatment. The treatment is cheap and has no side effects. Patients with the disease who don't receive treatment have a high risk of mortality.
It is of primary importance that your diagnostic tool has which of the following?
- A. High negative predictive value
- B. High positive predictive value
- C. Low false positive rate
- D. Low false negative rate
Answer: D
Explanation:
Explanation
A false negative is an error where a positive case (belonging to the target class) is incorrectly predicted as negative (not belonging to the target class). A false negative rate is the ratio of false negatives to all actual positive cases. A low false negative rate means that most of the positive cases are correctly identified by the classifier.
For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can receive treatment, it is of primary importance that it has a low false negative rate. This is because false negatives can have serious consequences for patients who have the disease but do not receive treatment, such as increased risk of mortality or complications. A low false negative rate can ensure that most patients who have the disease are diagnosed correctly and receive timely treatment.
NEW QUESTION # 29
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 training data used was inaccurate.
- B. The team set flawed expectations when training the model.
- C. The application was migrated from on-premise to a public cloud.
- D. The AI model was trained in winter and applied in summer.
Answer: D
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 # 30
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. Median Encoder
- B. Mean Encoder
- C. Log Encoder
- D. Count Encoder
- E. One-Hot Encoder
Answer: B,E
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 # 31
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. Recall and explained variance
- B. Precision and recall
- C. Precision and accuracy
- D. Mean squared error
Answer: B
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 # 32
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 collected accidentally.
- B. If it was with consent of the person it is collected from.
- C. If it was requested by the authority of law.
- D. If the data is only collected once.
- E. If it is for a good cause.
Answer: B,C
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 # 33
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?
- A. Neural network model, because this is a classification problem with a large number of features.
- B. A decision tree algorithm, because the problem is a classification problem with a small number of features.
- C. Logistic regression, because this is a classification problem and our data is linearly separable.
- D. k-means, because this is a clustering problem with a small number of features.
Answer: A
Explanation:
Explanation
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]
NEW QUESTION # 34
An HR solutions firm is developing software for staffing agencies that uses machine learning.
The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?
- A. Preexisting
- B. Technical
- C. Automation
- D. Emergent
Answer: A
Explanation:
Explanation
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.
NEW QUESTION # 35
Which three security measures could be applied in different ML workflow stages to defend them against malicious activities? (Select three.)
- A. Use data encryption.
- B. Use max privilege to control access to ML artifacts.
- C. Use Secrets Manager to protect credentials.
- D. Launch ML Instances In a virtual private cloud (VPC).
- E. Monitor model degradation.
- F. Disable logging for model access.
Answer: A,C,D
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 # 36
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. Clustering
- D. Polynomial regression
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 # 37
Which of the following tools would you use to create a natural language processing application?
- A. Azure Search
- B. NLTK
- C. DeepDream
- D. AWS DeepRacer
Answer: B
Explanation:
Explanation
NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .
NEW QUESTION # 38
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