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質問 # 25
Which of the learning methodology applies conditional probability of all the variables with respec-tive the dependent variable?
- A. Unsupervised learning
- B. Reinforcement learning
- C. Artificial learning
- D. Supervised learning
正解：B
解説：
Explanation
Supervised learning methodology applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few random experiments.
Conditional probability is thus the likelihood of an event or outcome occurring based on the occurrence of some other event or prior outcome. Two events are said tobe independent if one event occurring does not affect the probability that the other event will occur.
質問 # 26
Which Python method can be used to Remove duplicates by Data scientist?
- A. duplicates()
- B. clean_duplicates()
- C. remove_duplicates()
- D. drop_duplicates()
正解：B
解説：
Explanation
The drop_duplicates() method removes duplicate rows.
dataframe.drop_duplicates(subset, keep, inplace, ignore_index)
Remove duplicate rows from the DataFrame:
1.import pandas as pd
2.data = {
3."name": ["Peter", "Mary", "John", "Mary"],
4."age": [50, 40, 30, 40],
5."qualified": [True, False, False, False]
6.}
7.
8.df = pd.DataFrame(data)
9.newdf = df.drop_duplicates()
質問 # 27
Which of the following method is used for multiclass classification?
- A. one vs another
- B. loocv
- C. all vs one
- D. one vs rest
正解：D
解説：
Explanation
Binary vs. Multi-Class Classification
Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.
As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data.
The technique enables developers to categorize the test data into multiple binary class labels.
That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.
One-Vs-Rest Classification Model for Multi-Class Classification
Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.
For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:
Problem one: red vs. green/blue
Problem two: blue vs. green/red
Problem three: green vs. blue/red
The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.
The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used forclassification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.
質問 # 28
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the aggregate method shown in below code do?
g = df.groupby(df.index.str.len())
g.aggregate({'A':len, 'B':np.sum})
- A. Computes length of column A
- B. Computes Sum of column A values
- C. Computes length of column A and Sum of Column B values
- D. Computes length of column A and Sum of Column B values of each group
正解：D
解説：
Explanation
Computes length of column A and Sum of Column B values of each group
質問 # 29
Which of the Following is not type of Windows function in Snowflake?
- A. Window frame functions.
- B. Association functions.
- C. Rank-related functions.
- D. Aggregation window functions.
正解：B、D
解説：
Explanation
Window Functions
A window function operates on a group ("window") of related rows.
Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function.
Some window functions are order-sensitive. There are two main types of order-sensitive window functions:
Rank-related functions.
Window frame functions.
Rank-related functions list information based on the "rank" of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.
Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.
質問 # 30
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the expression g = df.groupby(df.index.str.len()) do?
- A. Data frames cannot be grouped by index values. Hence it results in Error.
- B. Groups df based on index values
- C. Groups df based on index strings
- D. Groups df based on length of each index value
正解：A
解説：
Explanation
Data frames cannot be grouped by index values. Hence it results in Error.
質問 # 31
To return the contents of a DataFrame as a Pandas DataFrame, Which of the following method can be used in SnowPark API?
- A. CONVERT_TO_PANDAS
- B. TO_PANDAS
- C. REPLACE_TO_PANDAS
- D. SNOWPARK_TO_PANDAS
正解：B
解説：
Explanation
To return the contents of a DataFrame as a Pandas DataFrame, use the to_pandas method.
For example:
1.>>> python_df = session.create_dataframe(["a", "b", "c"])
2.>>> pandas_df = python_df.to_pandas()
質問 # 32
Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?
- A. Leave-one-out cross-validation
- B. k-fold cross-validation
- C. Holdout method
- D. All of the above
正解：A
解説：
Explanation
Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.
Cross validation
It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.
Leave-one-out cross validation
Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.
質問 # 33
Which command is used to install Jupyter Notebook?
- A. pip install jupyter-notebook
- B. pip install jupyter
- C. pip install nbconvert
- D. pip install notebook
正解：B
解説：
Explanation
Jupyter Notebook is a web-based interactive computational environment.
The command used to install Jupyter Notebook is pip install jupyter.
The command used to start Jupyter Notebook is jupyter notebook.
質問 # 34
Which one is not the feature engineering techniques used in ML data science world?
- A. Imputation
- B. Binning
- C. Statistical
- D. One hot encoding
正解：C
解説：
Explanation
Feature engineering is the pre-processing step of machine learning, which is used to transform raw data into features that can be used for creating a predictive model using Machine learning or statistical Modelling.
What is a feature?
Generally, all machine learning algorithms take input data to generate the output. The input data re-mains in a tabular form consisting of rows (instances or observations) and columns (variable or at-tributes), and these attributes are often known as features. For example, an image is an instance in computer vision, but a line in the image could be the feature. Similarly, in NLP, a document can be an observation, and the word count could be the feature. So, we can say a feature is an attribute that impacts a problem or is useful for the problem.
What is Feature Engineering?
Feature engineering is the pre-processing step of machine learning, which extracts features from raw data. It helps to represent an underlying problem to predictive models in a better way, which as a result, improve the accuracy of the model for unseen data. The predictive model contains predictor variables and an outcome variable, and while the feature engineering process selects the most useful predictor variables for the model.
Some of the popular feature engineering techniques include:
1. Imputation
Feature engineering deals with inappropriate data, missing values,human interruption, general errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance of the algorithm, and to deal with them "Imputation" technique is used. Imputation is responsible for handling irregularities within the dataset.
For example, removing the missing values from the complete row or complete column by a huge percentage of missing values. But at the same time, to maintain the data size, it is required to impute the missing data, which can be done as:
For numerical data imputation, a default value can be imputed in a column, and missing values can be filled with means or medians of the columns.
For categorical data imputation, missing values can be interchanged with the maximum occurred value in a column.
2. Handling Outliers
Outliers are the deviated values or data points that are observed too away from other data points in such a way that they badly affect the performance of the model. Outliers can be handled with this feature engineering technique. This technique first identifies the outliers and then remove them out.
Standard deviation can be used to identify the outliers. For example, each value within a space has a definite to an average distance, but if a value is greater distant than acertain value, it can be considered as an outlier.
Z-score can also be used to detect outliers.
3. Log transform
Logarithm transformation or log transform is one of the commonly used mathematical techniques in machine learning. Log transform helps in handling the skewed data, and it makes the distribution more approximate to normal after transformation. It also reduces the effects of outliers on the data, as because of the normalization of magnitude differences, a model becomes much robust.
4. Binning
In machine learning, overfitting is one of the main issues that degrade the performance of the model and which occurs due to a greater number of parameters and noisydata. However, one of the popular techniques of feature engineering, "binning", can be used to normalize the noisy data. This process involves segmenting different features into bins.
5. Feature Split
As the name suggests, feature split is the process of splitting features intimately into two or more parts and performing to make new features. This technique helps the algorithms to better understand and learn the patterns in the dataset.
The feature splitting process enables the new features to be clustered and binned, which results in extracting useful information and improving the performance of the data models.
6. One hot encoding
One hot encoding is the popular encoding technique in machine learning. It is a technique that converts the categorical data in a form so that they can be easily understood by machine learning algorithms and hence can make a good prediction. It enables group theof categorical data without losing any information.
質問 # 35
Which type of Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series?
- A. Scaler Python UDFs
- B. Hybrid Python UDFs
- C. MPP Python UDFs
- D. Vectorized Python UDFs
正解：D
解説：
Explanation
Vectorized Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series. You call vectorized Py-thon UDFs the same way you call other Python UDFs.
Advantages of using vectorized Python UDFs compared to the default row-by-row processing pat-tern include:
The potential for better performance if your Python code operates efficiently on batches of rows.
Less transformation logic required if you are calling into libraries that operate on Pandas Data-Frames or Pandas arrays.
When you use vectorized Python UDFs:
You do not need to change how you write queries using Python UDFs. All batching is handled by the UDF framework rather than your own code.
As with non-vectorized UDFs, there is no guarantee of which instances of your handler code will see which batches of input.
質問 # 36
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
- A. Combine new datasets with your existing data in Snowflake to derive new business in-sights.
- B. Receive frictionless access to raw data products from vendors.
- C. Discover and test third-party data sources.
- D. Use the business intelligence (BI)/ML/Deep learning tools of her choice.
正解：A、B、C、D
解説：
Explanation
As a consumer, you can do the following:
Discover and test third-party data sources.
Receive frictionless access to raw data products from vendors.
Combine new datasets with your existing data in Snowflake to derive new business insights.
Have datasets available instantly and updated continually for users.
Eliminate the costs of building and maintaining various APIs and data pipelines to load and up-date data.
Use the business intelligence (BI) tools of your choice.
質問 # 37
All Snowpark ML modeling and preprocessing classes are in the ________ namespace?
- A. snowpark.ml.modeling
- B. snowflake.sklearn.modeling
- C. snowflake.scikit.modeling
- D. snowflake.ml.modeling
正解：D
解説：
Explanation
All Snowpark ML modeling and preprocessing classes are in the snowflake.ml.modeling namespace. The Snowpark ML modules have the same name as the corresponding module from the sklearn namespace. For example, the Snowpark ML module corresponding to sklearn.calibration is snow-flake.ml.modeling.calibration.
The xgboost and lightgbm modules correspond to snowflake.ml.modeling.xgboost and snow-flake.ml.modeling.lightgbm, respectively.
Not all of the classes from scikit-learn are supported in Snowpark ML.
質問 # 38
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?
- A. Data Mining
- B. Data visualization
- C. Data Interpretation
- D. Data Virtualization
正解：A
解説：
Explanation
Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics.
Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.
質問 # 39
Which tools helps data scientist to manage ML lifecycle & Model versioning?
- A. CRUX
- B. Albert
- C. MLFlow
- D. Pachyderm
正解：C、D
解説：
Explanation
Model versioning in a way involves tracking the changes made toan ML model that has been previously built.
Put differently, it is the process of making changes to the configurations of an ML Model. From another perspective, we can see model versioning as a feature that helps Machine Learning Engineers, Data Scientists, and related personnel create and keep multiple versions of the same model.
Think of it as a way of taking notes of the changes you make to the model through tweaking hyperparameters, retraining the model with more data, and so on.
In model versioning, a number of things need to be versioned, to help us keep track of important changes. I'll list and explain them below:
Implementation code: From the early days of model building to optimization stages, code or in this case source code of the model plays an important role. This code experiences significant changes during optimization stages which can easily be lost if not tracked properly. Because of this, code is one of the things that are taken into consideration during the model versioning process.
Data: In some cases, training data does improve significantly from its initial state during model op-timization phases. This can be as a result of engineering new features from existing ones to train our model on. Also there is metadata (data about your training data and model) to consider versioning. Metadata can change different times over without the training data actually changing. We need to be able to track these changes through versioning Model: The model is a product of the two previous entities and as stated in their explanations, an ML model changes at different points of the optimization phases through hyperparameter setting, model artifacts and learning coefficients. Versioning helps take record of the different versions of a Machine Learning model.
MLFlow & Pachyderm are the tools used to manage ML lifecycle & Model versioning.
質問 # 40
Which type of Machine learning Data Scientist generally used for solving classification and regression problems?
- A. Unsupervised
- B. Supervised
- C. Regression Learning
- D. Instructor Learning
- E. Reinforcement Learning
正解：B
解説：
Explanation
Supervised Learning
Overview:
Supervised learning is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs.
Algorithms:
Some of the most popularly used supervised learning algorithms are:
Linear Regression
Logistic Regression
Support Vector Machine
K Nearest Neighbor
Decision Tree
Random Forest
Naive Bayes
Working:
Supervised learning algorithms take labelled inputs and map them to the known outputs, which means you already know the target variable.
Supervised Learning methods need external supervision to train machine learning models. Hence, the name supervised. They need guidance and additional information to return the desired result.
Applications:
Supervised learning algorithms are generally used for solving classification and regression problems.
Few of the top supervised learning applications are weather prediction, sales forecasting, stock price analysis.
質問 # 41
Which of the following metrics are used to evaluate classification models?
- A. All of the above
- B. Area under the ROC curve
- C. F1 score
- D. Confusion matrix
正解：A
解説：
Explanation
Evaluation metrics are tied to machine learning tasks. There are different metrics for the tasks of classification and regression. Some metrics, like precision-recall, are useful for multiple tasks. Classification and regression are examples of supervised learning, which constitutes a majority of machine learning applications. Using different metrics for performance evaluation, we should be able to im-prove our model's overall predictive power before we roll it out for production on unseen data. Without doing a proper evaluation of the Machine Learning model by using different evaluation metrics, and only depending on accuracy, can lead to a problemwhen the respective model is deployed on unseen data and may end in poor predictions.
Classification metrics are evaluation measures used to assess the performance of a classification model.
Common metrics include accuracy (proportion of correct predictions), precision (true positives over total predicted positives), recall (true positives over total actual positives), F1 score (har-monic mean of precision and recall), and area under the receiver operating characteristic curve (AUC-ROC).
Confusion Matrix
Confusion Matrix is a performance measurement for the machine learning classification problems where the output can be two or more classes. It is a table with combinations of predicted and actual values.
It is extremely useful for measuring the Recall, Precision, Accuracy, and AUC-ROC curves.
The four commonly used metrics for evaluating classifier performance are:
1. Accuracy: The proportion of correct predictions out of the total predictions.
2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
These metrics help assess the classifier's effectiveness in correctly classifying instances of different classes.
Understanding how well a machine learning model will perform on unseen data is the main purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced then other methods like ROC/AUC perform better in evaluating the model performance.
ROC curve isn't just a single number but it's a whole curve that provides nuanced details about the behavior of the classifier. It is also hard to quickly compare many ROC curves to each other.
質問 # 42
Which one is incorrect understanding about Providers of Direct share?
- A. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts to consume.
- B. You can create as many shares as you want, and add as many accounts to a share as you want.
- C. As a data provider, you share a database with one or more Snowflake accounts.
- D. If you want to provide a share to many accounts, you can do the same via Direct Share.
正解：D
解説：
Explanation
If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.
質問 # 43
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