Snowflake DSA-C02 Dumps

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.

Explanation

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

Explanation

To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.

Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.

These are important metrics to track once models are deployed toproduction. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.

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.

Explanation

All are correct!

About the Snowflake Marketplace

You can use the Snowflake Marketplace to discover and access third-party data and services, as well as market your own data products across the Snowflake Data Cloud.

As a data provider, you can use listings on the Snowflake Marketplace to share curated data offer-ings with many consumers simultaneously, rather than maintain sharing relationships with each indi-vidual consumer. With Paid Listings, you can also charge for your data products.

As a consumer, you might use the data provided on the Snowflake Marketplace to explore and ac-cess the following:

Historical data for research, forecasting, and machine learning.

Up-to-date streaming data, such as current weather and traffic conditions.

Specialized identity data for understanding subscribers and audience targets.

New insights from unexpected sources of data.

The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with the exception of Mi-crosoft Azure Government. Support for Microsoft Azure Government is planned.

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.

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.

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.

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()

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()

Explanation

Type of visualization used for exploration:

· Correlation heatmap

· Class distributions by feature

· Two-Dimensional density plots.

All the visualizations are interactive, as is standard for Plotly.

For More details, please refer the below link:

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.

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.

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.

Explanation

Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML) visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.

Why Streamlit?

Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development time from days to hours. Following are some reasons to choose the Streamlit:

It is a free and open-source library.

Installing Streamlit is as simple as installing any other python package

It is easy to learn because you won’t need any web development experience, only a basic under-standing of Python is enough to build a data application.

It is compatible with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.

Explanation

It will Filters the row labelled r3.

Explanation

A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:

METADATA$ACTION

Indicates the DML operation (INSERT, DELETE) recorded.

METADATA$ISUPDATE

Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records inthe stream with a metadata column METADATA$ISUPDATE values set to TRUE.

Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.

METADATA$ROW_ID

Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.

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.

ExplanationFeature Scaling

Feature Scaling is the process of transforming the features so that they have a similar scale. This is important in machine learning because the scale of the features can affect the performance of the model.

Types of Feature Scaling:

Min-Max Scaling: Rescaling the features to a specific range, such as between 0 and 1, by subtracting the minimum value and dividing by the range.

Standard Scaling: Rescaling the features to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.

Robust Scaling: Rescaling the features to be robust to outliers by dividing them by the interquartile range.

Benefits of Feature Scaling:

Improves Model Performance: By transforming the features to have a similar scale, the model can learn from all features equally and avoid being dominated by a few large features.

Increases Model Robustness: By transforming the features to be robust to outliers, the model can become more robust to anomalies.

Improves Computational Efficiency: Many machine learning algorithms, such as k-nearest neighbors, are sensitive to the scale of the features and perform better with scaled features.

Improves Model Interpretability: By transforming the features to have a similar scale, it can be easier to understand the model’s predictions.