Snowflake SnowPro Advanced: Data Scientist Certification DSA-C02 Exam Questions

Answer : A, C

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.

Answer : D

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.

Answer : D

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 a certain 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 noisy data. 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 the of categorical data without losing any information.

Answer : C

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 root task 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.

Answer : B

To know about lateral flatten SQL Function, please refer:

https://docs.snowflake.com/en/sql-reference/constructs/join-lateral#example-of-using-lateral-with-flatten

Answer : D

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.

Answer : C

Moving Data from Spark to Snowflake

The steps for saving the contents of a DataFrame to a Snowflake table are similar to writing from Snowflake to Spark:

1. Use the write() method of the DataFrame to construct a DataFrameWriter.

2. Specify SNOWFLAKE_SOURCE_NAME using the format() method.

3. Specify the connector options using either the option() or options() method.

4. Use the dbtable option to specify the table to which data is written.

5. Use the mode() method to specify the save mode for the content.

Examples

1. df.write

2. .format(SNOWFLAKE_SOURCE_NAME)

3. .options(sfOptions)

4. .option('dbtable', 't2')

5. .mode(SaveMode.Overwrite)

6. .save()