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質問 # 19
Which of the learning methodology applies conditional probability of all the variables with respec-tive the dependent variable?
- A. Supervised learning
- B. Artificial learning
- C. Reinforcement learning
- D. Unsupervised learning
正解：C
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 # 20
Select the Correct Statements regarding Normalization?
- A. Normalization technique uses minimum and max values for scaling of model.
- B. Scikit-Learn provides a transformer RecommendedScaler for Normalization.
- C. Normalization technique uses mean and standard deviation for scaling of model.
- D. Normalization got affected by outliers.
正解：A、D
解説：
Explanation
Normalization is a scaling technique in Machine Learning applied during data preparation to change the values of numeric columns in the dataset to use a common scale.It is not necessary for all datasets in a model. It is required only when features of machine learning models have different ranges.
Scikit-Learn provides a transformer called MinMaxScaler for Normalization.
This technique uses minimum and max values for scaling of model.Itis useful when feature distribution is unknown.It got affected by outliers.
質問 # 21
All aggregate functions except _____ ignore null values in their input collection
- A. Count(attribute)
- B. Count(*)
- C. Sum
- D. Avg
正解：B
解説：
Explanation
Count(*)
* is used to select all values including null.
質問 # 22
Which type of Machine learning Data Scientist generally used for solving classification and regression problems?
- A. Regression Learning
- B. Instructor Learning
- C. Unsupervised
- D. Reinforcement Learning
- E. Supervised
正解：E
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 # 23
Mark the incorrect statement regarding usage of Snowflake Stream & Tasks?
- A. Snowflake ensures only one instance of a task with a schedule (i.e. a standalone task or the root task in a DAG) is executed at a given time. If a task is still running when the next scheduled execution time occurs, then that scheduled time is skipped.
- B. Streams support repeatable read isolation.
- C. An standard-only stream tracks row inserts only.
- D. Snowflake automatically resizes and scales the compute resources for serverless tasks.
正解：C
解説：
Explanation
All are correct except a standard-only stream tracks row inserts only.
A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, up-dates, and deletes (including table truncates).
質問 # 24
Which of the following method is used for multiclass classification?
- A. one vs rest
- B. all vs one
- C. loocv
- D. one vs another
正解：A
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 # 25
Which of the following cross validation versions is suitable quicker cross-validation for very large datasets with hundreds of thousands of samples?
- A. k-fold cross-validation
- B. Leave-one-out cross-validation
- C. Holdout method
- D. All of the above
正解：C
解説：
Explanation
Holdout cross-validation method is suitable for very large dataset because it is the simplest and quicker to compute version of cross-validation.
Holdout method
In this method, the dataset is divided into two sets namely the training and the test set with the basic property that the training set is bigger than the test set. Later, the model is trained on the training dataset and evaluated using the test dataset.
質問 # 26
A Data Scientist as data providers require to allow consumers to access all databases and database objects in a share by granting a single privilege on shared databases. Which one is incorrect SnowSQL command used by her while doing this task?
Assuming:
A database named product_db exists with a schema named product_agg and a table named Item_agg.
The database, schema, and table will be shared with two accounts named xy12345 and yz23456.
1.USE ROLE accountadmin;
2.CREATE DIRECT SHARE product_s;
3.GRANT USAGE ON DATABASE product_db TO SHARE product_s;
4.GRANT USAGE ON SCHEMA product_db. product_agg TO SHARE product_s;
5.GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
6.SHOW GRANTS TO SHARE product_s;
7.ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
8.SHOW GRANTS OF SHARE product_s;
- A. GRANT USAGE ON DATABASE product_db TO SHARE product_s;
- B. CREATE DIRECT SHARE product_s;
- C. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
- D. ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
正解：C
解説：
Explanation
CREATE SHARE product_s is the correct Snowsql command to create Share object.
Rest are correct ones.
https://docs.snowflake.com/en/user-guide/data-sharing-provider#creating-a-share-using-sql
質問 # 27
Secure Data Sharing do not let you share which of the following selected objects in a database in your account with other Snowflake accounts?
- A. Tables
- B. Sequences
- C. External tables
- D. Secure UDFs
正解：B
解説：
Explanation
Secure Data Sharing lets you share selected objects in a database in your account with other Snow-flake accounts. You can share the following Snowflake database objects:
Tables
External tables
Secure views
Secure materialized views
Secure UDFs
Snowflake enables the sharing of databases through shares, which are created by data providers and
"imported" by data consumers.
質問 # 28
Which one is the incorrect option to share data in Snowflake?
- A. a Direct Share, in which you directly share specific database objects (a share) to anoth-er account in your region.
- B. a Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
- C. a Direct Marketplace, in which you directly share specific database objects (a share) to another account in your region using Snowflake Marketplace.
- D. a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
正解：C
解説：
Explanation
Options for Sharing in Snowflake
You can share data in Snowflake using one of the following options:
a Listing, in which you offer a share and additional metadata as a data product to one or more ac-counts,
a Direct Share, in which you directly share specific database objects (a share) to another account in your region,
a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
質問 # 29
There are a couple of different types of classification tasks in machine learning, Choose the Correct Classification which best categorized the below Application Tasks in Machine learning?
To detect whether email is spam or not
To determine whether or not a patient has a certain disease in medicine.
To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).
- A. Binary Classification
- B. Multi-Class Classification
- C. Logistic Regression
- D. Multi-Label Classification
正解：A
解説：
Explanation
The Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms.
What is the Classification Algorithm?
The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. Such as, Yes or No, 0 or
1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories.
Unlike regression, the output variable of Classification is a category, not a value, such as "Green or Blue",
"fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output.
In classification algorithm, a discrete output function(y) is mapped to input variable(x).
y=f(x), where y = categorical output
The best example of an ML classification algorithm is Email Spam Detector.
The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.
The algorithm which implements the classification on a dataset is known as a classifier. There are two types of Classifications:
Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier.
Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc.
Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier.
Example: Classifications of types of crops, Classification of types of music.
Binary classification in deep learning refers to the type of classification where we have two class labels - one normal and one abnormal. Some examples of binary classification use:
To detect whether email is spam or not
To determine whether or not a patient has a certain disease in medicine.
To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).
For example, the normal class label would be that a patient has the disease, and the abnormal class label would be that they do not, or vice-versa.
As is with every other type of classification, it is only as good as the binary classification dataset that it has - or, in other words, the more training and data it has, the better it is.
質問 # 30
All Snowpark ML modeling and preprocessing classes are in the ________ namespace?
- A. snowflake.ml.modeling
- B. snowpark.ml.modeling
- C. snowflake.scikit.modeling
- D. snowflake.sklearn.modeling
正解：A
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 # 31
Data Scientist can query, process, and transform data in a which of the following ways using Snowpark Python. [Select 2]
- A. Query and process data with a DataFrame object.
- B. SnowPark currently do not support writing UDTF.
- C. Transform Data using DataIKY tool with SnowPark API.
- D. Write a user-defined tabular function (UDTF) that processes data and returns data in a set of rows with one or more columns.
正解：A、B
解説：
Explanation
Query and process data with a DataFrame object. Refer to Working with DataFrames in Snowpark Python.
Convert custom lambdas and functions to user-defined functions(UDFs) that you can call to process data.
Write a user-defined tabular function (UDTF) that processes data and returns data in a set of rows with one or more columns.
Write a stored procedure that you can call to process data, or automate with a task to build a data pipeline.
質問 # 32
Which one is not the feature engineering techniques used in ML data science world?
- A. Statistical
- B. One hot encoding
- C. Binning
- D. Imputation
正解：A
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 # 33
Which of the following metrics are used to evaluate classification models?
- A. F1 score
- B. All of the above
- C. Area under the ROC curve
- D. Confusion matrix
正解：B
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 # 34
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