[2024年01月19日] 有効なAIP-210テスト解答AIP-210試験PDF問題を試そう
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CertNexus AIP-210 認定試験の出題範囲：
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質問 # 22
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.
正解：C
解説：
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]
質問 # 23
Which type of regression represents the following formula: y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable?
- A. Linear regression
- B. Lasso regression
- C. Ridge regression
- D. Polynomial regression
正解：A
質問 # 24
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. Add 1 to all of the Y values.
- B. Explore the data for outliers.
- C. Subtract the mean of Y from all the Y values.
- D. Divide all the Y values by the standard deviation of Y.
正解：A
解説：
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.
質問 # 25
A dataset can contain a range of values that depict a certain characteristic, such as grades on tests in a class during the semester. A specific student has so far received the following grades: 76,81, 78, 87, 75, and 72.
There is one final test in the semester. What minimum grade would the student need to achieve on the last test to get an 80% average?
- A. 0
- B. 1
- C. 2
- D. 3
正解：D
解説：
Explanation
To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:
minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1) Plugging in the given values, we get:
minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)
minimum grade = (560 - 469) / 1
minimum grade = 91
Therefore, the student needs to score at least 91 on the last test to get an 80% average.
質問 # 26
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
正解：D
解説：
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
質問 # 27
Which of the following models are text vectorization methods? (Select two.)
- A. t-SNE
- B. TF-IDF
- C. PCA
- D. Skip-gram
- E. Tokenization
- F. Lemmatization
正解：B、D
解説：
Explanation
Skip-gram and TF-IDF are both text vectorization methods that convert text into numerical feature vectors.
Skip-gram is a prediction-based word embedding method that learns vector representations of words from their contexts in a large corpus of text. TF-IDF is a frequency-based word weighting method that assigns scores to words based on their importance in a document and in a corpus of documents. References: Text Vectorization and Word Embedding | Guide to Master NLP (Part 5), What Is Text Vectorization? Everything You Need to Know - deepset
質問 # 28
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. Clustering
- C. Ridge regression
- D. Polynomial regression
正解：C
解説：
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
質問 # 29
Which of the following describes a neural network without an activation function?
- A. A form of a linear regression
- B. A radial basis function kernel
- C. A form of a quantile regression
- D. An unsupervised learning technique
正解：A
解説：
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.
質問 # 30
A change in the relationship between the target variable and input features is
- A. covariate shift.
- B. model decay.
- C. concept drift.
- D. data drift.
正解：C
解説：
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
質問 # 31
Which of the following can take a question in natural language and return a precise answer to the question?
- A. Pandas
- B. Databricks
- C. Spark ML
- D. IBM Watson
正解：D
解説：
Explanation
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.
質問 # 32
Which two of the following decrease technical debt in ML systems? (Select two.)
- A. Boundary erosion
- B. Model complexity
- C. Design anti-patterns
- D. Refactoring
- E. Documentation readability
正解：D、E
解説：
Explanation
Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to various factors, such as changing requirements, outdated code, poor documentation, or lack of testing. Some of the ways to decrease technical debt in ML systems are:
Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent information about the system's design, functionality, performance, and maintenance. Documentation readability can also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and managers.
Refactoring: Refactoring is the process of improving the structure and quality of code without changing its functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity, or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.
質問 # 33
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)
正解：D
解説：
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.
質問 # 34
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A. Word2Vec algorithm
- B. Bag of words model with TF-IDF
- C. Clustering similar words and representing words by group membership
- D. Bag of bigrams (2 letter pairs)
正解：D
解説：
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 .
質問 # 35
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.
正解：C
解説：
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]
質問 # 36
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Portability
- B. Explainability
- C. Data size
- D. Complexity
- E. Scalability
正解：B、E
解説：
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.
質問 # 37
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+7)
- B. 37/(37+8)
- C. 48/(48+37)
- D. (48+37)/100
正解：B
解説：
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.
質問 # 38
Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?
- A. Algorithmic bias
- B. Sampling bias
- C. Systematic value distortion
- D. Stereotype bias
正解：B
解説：
Explanation
Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]
質問 # 39
Which of the following is a type 1 error in statistical hypothesis testing?
- A. The null hypothesis is true, but is rejected.
- B. The null hypothesis is true and fails to be rejected.
- C. The null hypothesis is false and is rejected.
- D. The null hypothesis is false, but fails to be rejected.
正解：A
解説：
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]
質問 # 40
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. Understanding, documenting, and displaying data lineage.
- C. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
- D. Collecting and processing the largest amount of data possible.
正解：B
解説：
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 .
質問 # 41
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
- A. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
- B. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
- C. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- D. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
正解：A
解説：
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.
質問 # 42
Which of the following is NOT a valid cross-validation method?
- A. Leave-one-out
- B. Stratification
- C. Bootstrapping
- D. K-fold
正解：B
解説：
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.
質問 # 43
Word Embedding describes a task in natural language processing (NLP) where:
- A. Words are converted into numerical vectors.
- B. Words are featurized by taking a matrix of bigram counts.
- C. Words are featurized by taking a histogram of letter counts.
- D. Words are grouped together into clusters and then represented by word cluster membership.
正解：A
解説：
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.
質問 # 44
Workflow design patterns for the machine learning pipelines:
- A. Aim to explain how the machine learning model works.
- B. Seek to simplify the management of machine learning features.
- C. Represent a pipeline with directed acyclic graph (DAG).
- D. Separate inputs from features.
正解：C
解説：
Explanation
Workflow design patterns for machine learning pipelines are common solutions to recurring problems in building and managing machine learning workflows. One of these patterns is to represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and edges, where each node represents a step or task in the pipeline, and each edge represents a dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at one node and return to it by following the edges. A DAG can help visualize and organize the pipeline, as well as facilitate parallel execution, fault tolerance, and reproducibility.
質問 # 45
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