DSA-C02 Online Practice Questions and Answers

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: 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.

Correct Answer: ABCD

Correct Answer: C

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

Correct Answer: C

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.

Correct Answer: C

Explanation:

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

Correct Answer: D

Explanation: Data frames cannot be grouped by index values. Hence it results in Error.

Correct Answer: B

Explanation:

Hyperparameters in Machine learning are those parameters that are explicitly defined by the user to control the learning process. These hyperparameters are used to improve the learning of the model, and their values are set before starting

the learning process of the model.

What are hyperparameters?

In Machine Learning/Deep Learning, a model is represented by its parameters. In contrast, a training process involves selecting the best/optimal hyperparameters that are used by learning algorithms to provide the best result. So, what are

these hyperparameters? The answer is, "Hyperparameters are defined as the parameters that are explicitly defined by the user to control the learning process."

Here the prefix "hyper" suggests that the parameters are top-level parameters that are used in con-trolling the learning process. The value of the Hyperparameter is selected and set by the machine learning engineer before the learning

algorithm begins training the model. Hence, these are external to the model, and their values cannot be changed during the training process.

Some examples of Hyperparameters in Machine Learning ?The k in kNN or K-Nearest Neighbour algorithm

Learning rate for training a neural network

Train-test split ratio

Batch Size

Number of Epochs

Branches in Decision Tree

Number of clusters in Clustering Algorithm

Model Parameters:

Model parameters are configuration variables that are internal to the model, and a model learns them on its own. For example, W Weights or Coefficients of independentvariables in the Linear regression model. or Weights or Coefficients of

independent variables in SVM, weight, and biases of a neural network, cluster centroid in clustering. Some key points for model parameters are as follows:

They are used by the model for making predictions.

They are learned by the model from the data itself.

These are usually not set manually.

These are the part of the model and key to a machine learning Algorithm.

Model Hyperparameters:

Hyperparameters are those parameters that are explicitly defined by the user to control the learning process. Some key points for model parameters are as follows:

These are usually defined manually by the machine learning engineer. One cannot know the exact best value for hyperparameters for the given problem. The best value can be determined either by the rule of thumb or by trial and error. Some

examples of Hyperparameters are the learning rate for training a neural network, K in the KNN algorithm.

Correct Answer: D

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).

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.