SnowPro Advanced Certification DSA-C02 Exam Questions and Answers PDF

Explanation

Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views.

stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, several hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables.

Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.

The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise it stays at the same position.

Explanation

Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model’s predictions.

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.

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.