DSA-C02 Online Practice Questions and Answers

Correct Answer: AD

Explanation:

Normalization is a scaling technique in Machine Learning applied during data preparation to change the values of numeric columns in the dataset to use a common scale.It is not necessary for all datasets in a model. It is required only when

features of machine learning models have different ranges.

Scikit-Learn provides a transformer called MinMaxScaler for Normalization. This technique uses minimum and max values for scaling of model.Itis useful when feature distribution is unknown.It got affected by outliers.

Correct Answer: B

Explanation: Count(*)

* is used to select all values including null.

Correct Answer: C

Explanation:

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

Correct Answer: D

Explanation:

Hex -- collaborative data science and analytics platform Denodo -- data virtualization and federation platform DvSum -- data catalog and data intelligence platform Diyotta -- data integration and migration

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:

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))). Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.

Correct Answer: ABCD

Explanation:

From the perspective of a user running a SQL statement, an external function behaves like any other UDF . External functions follow these rules:

External functions return a value.

External functions can accept parameters.

An external function can appear in any clause of a SQL statement in which other types of UDF can appear. For example:

1.select my_external_function_2(column_1, column_2) 2.from table_1;

1.select col1

2.from table_1

3.where my_external_function_3(col2) < 0;

1.create view view1 (col1) as

2.select my_external_function_5(col1)

3.from table9;

An external function can be part of a more complex expression:

1.select upper(zipcode_to_city_external_function(zipcode)) 2.from address_table;

The returned value can be a compound value, such as a VARIANT that contains JSON. External functions can be overloaded; two different functions can have the same name but different signatures (different numbers or data types of input

parameters).

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.

Correct Answer: D

Explanation:

The area under the ROC curve (AUC) is a common evaluation metric for binary classification, which measures the performance of a classifier at different threshold values for the predicted probabilities. Other common metrics include

accuracy, precision, recall, and F1 score, which are based on the confusion matrix of true positives, false positives, true negatives, and false negatives.