Snowflake Snowflake Certifications DSA-C02 Questions & Answers

• Question 11:

  Which command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task?

  A. RUN TASK

  B. CALL TASK

  C. EXECUTE TASK

  D. RUN ROOT TASK

  Correct Answer: C

  Explanation: The EXECUTE TASK command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task. A successful run of a roottask triggers a cascading run of child tasks in the DAG as their precedent task completes, as though the root task had run on its defined schedule. This SQL command is useful for testing new or modified standalone tasks and DAGs before you enable them to execute SQL code in production. Call this SQL command directly in scripts or in stored procedures. In addition, this command sup-ports integrating tasks in external data pipelines. Any third-party services that can authenticate into your Snowflake account and authorize SQL actions can execute the EXECUTE TASK command to run tasks.

• Question 12:

  Which one is not the feature engineering techniques used in ML data science world?

  A. Imputation

  B. Binning

  C. One hot encoding

  D. Statistical

  Correct Answer: D

  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.

• Question 13:

  Which type of Machine learning Data Scientist generally used for solving classification and regression problems?

  A. Supervised

  B. Unsupervised

  C. Reinforcement Learning

  D. Instructor Learning

  E. Regression Learning

  Correct Answer: A

  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.

• Question 14:

  Which of the following method is used for multiclass classification?

  A. one vs rest

  B. loocv

  C. all vs one

  D. one vs another

  Correct Answer: A

  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.

• Question 15:

  Performance metrics are a part of every machine learning pipeline, Which ones are not the performance metrics used in the Machine learning?

  A. R - (R-Squared)

  B. Root Mean Squared Error (RMSE)

  C. AU-ROC

  D. AUM

  Correct Answer: D

• Question 16:

  Which is the visual depiction of data through the use of graphs, plots, and informational graphics?

  A. Data Interpretation

  B. Data Virtualization

  C. Data visualization

  D. Data Mining

  Correct Answer: D

  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.

• Question 17:

  Mark the Incorrect statements regarding MIN / MAX Functions?

  A. NULL values are skipped unless all the records are NULL

  B. NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

  C. The data type of the returned value is the same as the data type of the input values

  D. For compatibility with other systems, the DISTINCT keyword can be specified as an argument for MIN or MAX, but it does not have any effect

  Correct Answer: B

  Explanation:

  NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

• Question 18:

  Which command is used to install Jupyter Notebook?

  A. pip install jupyter

  B. pip install notebook

  C. pip install jupyter-notebook

  D. pip install nbconvert

  Correct Answer: A

  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.

• Question 19:

  Which Python method can be used to Remove duplicates by Data scientist?

  A. remove_duplicates()

  B. duplicates()

  C. drop_duplicates()

  D. clean_duplicates()

  Correct Answer: D

  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()

• Question 20:

  Which tools helps data scientist to manage ML lifecycle and Model versioning? Choose 2.

  A. MLFlow

  B. Pachyderm

  C. Albert

  D. CRUX

  Correct Answer: AB

  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 and Pachyderm are the tools used to manage ML lifecycle and Model versioning.