Correct Answer: C

Explanation:

Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

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.

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.

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.

Correct Answer: AC

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.

Correct Answer: ABCD

Correct Answer: D

Explanation:

A scalar function (UDF) returns one output row for each input row. The returned row consists of a single column/value

Correct Answer: C

Explanation:

Computes length of column A and Sum of Column B values of each group

Correct Answer: ABCD

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.

Correct Answer: C

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.