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NEW QUESTION # 29
Which one is not the types of Feature Engineering Transformation?

• A. Scaling
• B. Encoding
• C. Normalization
• D. Aggregation

Answer: D

Explanation:
Explanation
What is Feature Engineering?
Feature engineering is the process of transforming raw data into features that are suitable for ma-chine learning models. In other words, it is the process of selecting, extracting, and transforming the most relevant features from the available data to build more accurate and efficient machine learning models.
The success of machine learning models heavily depends on the quality of the features used to train them.
Feature engineering involves a set of techniques that enable us to create new features by combining or transforming the existing ones. These techniques help to highlight the most important pat-terns and relationships in the data, which in turn helps the machine learning model to learn from the data more effectively.
What is a Feature?
In the context of machine learning, a feature (also known as a variable or attribute) is an individual measurable property or characteristic of a data point that is used as input for a machine learning al-gorithm. Features can be numerical, categorical, or text-based, and they represent different aspects of the data that are relevant to the problem at hand.
For example, in a dataset of housing prices, features could include the number of bedrooms, the square footage, the location, and the age of the property. In a dataset of customer demographics, features could include age, gender, income level, and occupation.
The choice and quality of features are critical in machine learning, as they can greatly impact the ac-curacy and performance of the model.
Why do we Engineer Features?
We engineer features to improve the performance of machine learning models by providing them with relevant and informative input data. Raw data may contain noise, irrelevant information, or missing values, which can lead to inaccurate or biased model predictions. By engineering features, we can extract meaningful information from the raw data, create new variables that capture important patterns and relationships, and transform the data into a more suitable format for machine learning algorithms.
Feature engineering can also help in addressing issues such as overfitting, underfitting, and high di-mensionality. For example, by reducing the number of features, we can prevent the model from be-coming too complex or overfitting to the training data. By selecting the most relevant features, we can improve the model's accuracy and interpretability.
In addition, feature engineering is a crucial step in preparing data for analysis and decision-making in various fields, such as finance, healthcare, marketing, and social sciences. It can help uncover hidden insights, identify trends and patterns, and support data-driven decision-making.
We engineer features for various reasons, and some of the main reasons include:
Improve User Experience: The primary reason we engineer features is to enhance the user experience of a product or service. By adding new features, we can make the product more intuitive, efficient, and user-friendly, which can increase user satisfaction and engagement.
Competitive Advantage: Another reason we engineer features is to gain a competitive advantage in the marketplace. By offering unique and innovative features, we can differentiate our product from competitors and attract more customers.
Meet Customer Needs: We engineer features to meet the evolving needs of customers. By analyzing user feedback, market trends, and customer behavior, we can identify areas where new features could enhance the product's value and meet customer needs.
Increase Revenue: Features can also be engineered to generate more revenue. For example, a new feature that streamlines the checkout process can increase sales, or a feature that provides additional functionality could lead to more upsells or cross-sells.
Future-Proofing: Engineering features can also be done to future-proof a product or service. By an-ticipating future trends and potential customer needs, we can develop features that ensure the product remains relevant and useful in the long term.
Processes Involved in Feature Engineering
Feature engineering in Machine learning consists of mainly 5 processes: Feature Creation, Feature Transformation, Feature Extraction, Feature Selection, and Feature Scaling. It is an iterative process that requires experimentation and testing to find the best combination of features for a given problem. The success of a machine learning model largely depends on the quality of the features used in the model.
Feature Transformation
Feature Transformation is the process of transforming the featuresinto a more suitable representation for the machine learning model. This is done to ensure that the model can effectively learn from the data.
Types of Feature Transformation:
Normalization: Rescaling the features to have a similar range, such as between 0 and 1, to prevent some features from dominating others.
Scaling: Rescaling the features to have a similar scale, such as having a standard deviation of 1, to make sure the model considers all features equally.
Encoding: Transforming categorical features into a numerical representation. Examples are one-hot encoding and label encoding.
Transformation: Transforming the features using mathematical operations to change the distribution or scale of the features. Examples are logarithmic, square root, and reciprocal transformations.

NEW QUESTION # 30
In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?

• A. no change
• B. by its slope
• C. by 1
• D. by intercept

Answer: B

Explanation:
Explanation
What is linear regression?
Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatoryvariable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
For linear regression Y=a+bx+error.
If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.
For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

NEW QUESTION # 31
Which method is used for detecting data outliers in Machine learning?

• A. Scaler
• B. CMIYC
• C. Z-Score
• D. BOXI

Answer: C

Explanation:
Explanation
What are outliers?
Outliers are the values that look different from the other values in the data. Below is a plot high-lighting the outliers in 'red' and outliers can be seen in both the extremes of data.
Reasons for outliers in data
Errors during data entry or a faulty measuring device (a faulty sensor may result in extreme readings).
Natural occurrence (salaries of junior level employees vs C-level employees) Problems caused by outliers Outliers in the data may causes problems during model fitting (esp. linear models).
Outliers may inflate the error metrics which give higher weights to large errors (example, mean squared error, RMSE).
Z-score method is of the method for detecting outliers. This methodis generally used when a variable' distribution looks close to Gaussian. Z-score is the number of standard deviations a value of a variable is away from the variable' mean.
Z-Score = (X-mean) / Standard deviation
IQR method , Box plots are some more example of methods used to detect data outliers in Data science.

NEW QUESTION # 32
You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.
What should you do?

• A. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
• B. Retrained your training dataset after correcting data outliers & no need to introduce new data.
• C. Create a new version of the dataset using only the new data and retrain the model.
• D. Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.

Answer: D

Explanation:
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.

NEW QUESTION # 33
What is the formula for measuring skewness in a dataset?

• A. MODE - MEDIAN
• B. (3(MEAN - MEDIAN))/ STANDARD DEVIATION
• C. MEAN - MEDIAN
• D. (MEAN - MODE)/ STANDARD DEVIATION

Answer: B

Explanation:
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.

NEW QUESTION # 34
The most widely used metrics and tools to assess a classification model are:

• A. Cost-sensitive accuracy
• B. Area under the ROC curve
• C. All of the above
• D. Confusion matrix

Answer: C

NEW QUESTION # 35
Which one is not the feature engineering techniques used in ML data science world?

• A. Statistical
• B. One hot encoding
• C. Imputation
• D. Binning

Answer: A

Explanation:
Explanation
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 acertain 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 noisydata. 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 theof categorical data without losing any information.

NEW QUESTION # 36
Which of the following metrics are used to evaluate classification models?

• A. Area under the ROC curve
• B. All of the above
• C. Confusion matrix
• D. F1 score

Answer: B

Explanation:
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.

NEW QUESTION # 37
Which one is incorrect understanding about Providers of Direct share?

• A. If you want to provide a share to many accounts, you can do the same via Direct Share.
• B. You can create as many shares as you want, and add as many accounts to a share as you want.
• C. As a data provider, you share a database with one or more Snowflake accounts.
• D. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts to consume.

Answer: A

Explanation:
Explanation
If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.

NEW QUESTION # 38
Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share us-ing Which of the following options?

• A. Grant privileges on objects to a share via Account role.
• B. Grant privileges on objects to a share via a third-party role.
• C. Grant privileges on objects directly to a share.
• D. Grant privileges on objects to a share via a database role.

Answer: C,D

Explanation:
ExplanationWhat is a Share?
Shares are named Snowflake objects that encapsulate all of the information required to share a database.
Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share using either or both of the following options:
Option 1: Grant privileges on objects to a share via a database role.
Option 2: Grant privileges on objects directly to a share.
You choose which accounts can consume data from the share by adding the accounts to the share.
After a database is created (in a consumer account) from a share, all the shared objects are accessible to users in the consumer account.
Shares are secure, configurable, and controlled completely by the provider account:
New objects added to a share become immediately available to all consumers, providing real-time access to shared data.
Access to a share (or any of the objects in a share) can be revoked at any time.

NEW QUESTION # 39
Consider a data frame df with columns ['A', 'B', 'C', 'D'] and rows ['r1', 'r2', 'r3']. What does the ex-pression df[lambda x : x.index.str.endswith('3')] do?

• A. Returns the row name r3
• B. Results in Error
• C. Filters the row labelled r3
• D. Returns the third column

Answer: C

Explanation:
Explanation
It will Filters the row labelled r3.

NEW QUESTION # 40
Which type of 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?

• A. Scaler Python UDFs
• B. Vectorized Python UDFs
• C. MPP Python UDFs
• D. Hybrid Python UDFs

Answer: B

Explanation:
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.

NEW QUESTION # 41
All Snowpark ML modeling and preprocessing classes are in the ________ namespace?

• A. snowflake.sklearn.modeling
• B. snowpark.ml.modeling
• C. snowflake.scikit.modeling
• D. snowflake.ml.modeling

Answer: D

Explanation:
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.

NEW QUESTION # 42
You are training a binary classification model to support admission approval decisions for a college degree program.
How can you evaluate if the model is fair, and doesn't discriminate based on ethnicity?

• A. None of the above.
• B. Remove the ethnicity feature from the training dataset.
• C. Evaluate each trained model with a validation datasetand use the model with the highest accuracy score.
• D. Compare disparity between selection rates and performance metrics across ethnicities.

Answer: D

Explanation:
Explanation
By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.

NEW QUESTION # 43
Which one is not Types of Feature Scaling?

• A. Standard Scaling
• B. Economy Scaling
• C. Robust Scaling
• D. Min-Max Scaling

Answer: D

Explanation:
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.

NEW QUESTION # 44
Mark the correct steps for saving the contents of a DataFrame to aSnowflake table as part of Moving Data from Spark to Snowflake?

• A. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter.
  Step 2.Specify SNOWFLAKE_SOURCE_NAME using the NAME() method.
  Step 3.Use the dbtable option to specify the table to which data is written.
  Step 4.Specify the connector options using either the option() or options() method.
  Step 5.Use the save() method to specify the save mode for the content.
• B. Step 1.Use the writer() method of the DataFrame to construct a DataFrameWriter.
  Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
  Step 3.Use the dbtable option to specify the table to which data is written.
  Step 4.Specify the connector options using either the option() or options() method.
  Step 5.Use the save() method to specify the save mode for the content.
• C. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter.
  Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
  Step 3.Specify the connector options using either the option() or options() method.
  Step 4.Use the dbtable option to specify the table to which data is written.
  Step 5.Use the save() method to specify the save mode for the content.
• D. Step 1.Use the write() method of the DataFrame to construct a DataFrameWriter.
  Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
  Step 3.Specify the connector options using either the option() or options() method.
  Step 4.Use the dbtable option to specify the table to which data is written.
  Step 5.Use the mode() method to specify the save mode for the content.
  (Correct)

Answer: D

Explanation:
Explanation
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()

NEW QUESTION # 45
Mark the Incorrect statements regarding MIN / MAX Functions?

• A. For compatibility with other systems, the DISTINCT keyword can be specified as an argument for MIN or MAX, but it does not have any effect
• B. NULL values are skipped unless all the records are NULL
• C. The data type of the returned value is the same as the data type of the input values
• D. NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

Answer: D

Explanation:
Explanation
NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

NEW QUESTION # 46
A Data Scientist as data providers require to allow consumers to access all databases and database objects in a share by granting a single privilege on shared databases. Which one is incorrect SnowSQL command used by her while doing this task?
Assuming:
A database named product_db exists with a schema named product_agg and a table named Item_agg.
The database, schema, and table will be shared with two accounts named xy12345 and yz23456.
1.USE ROLE accountadmin;
2.CREATE DIRECT SHARE product_s;
3.GRANT USAGE ON DATABASE product_db TO SHARE product_s;
4.GRANT USAGE ON SCHEMA product_db. product_agg TO SHARE product_s;
5.GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
6.SHOW GRANTS TO SHARE product_s;
7.ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
8.SHOW GRANTS OF SHARE product_s;

• A. CREATE DIRECT SHARE product_s;
• B. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
• C. GRANT USAGE ON DATABASE product_db TO SHARE product_s;
• D. ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;

Answer: B

Explanation:
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

NEW QUESTION # 47
To return the contents of a DataFrame as a Pandas DataFrame, Which of the following method can be used in SnowPark API?

• A. CONVERT_TO_PANDAS
• B. REPLACE_TO_PANDAS
• C. TO_PANDAS
• D. SNOWPARK_TO_PANDAS

Answer: C

Explanation:
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()

NEW QUESTION # 48
Which command is used to install Jupyter Notebook?

• A. pip install jupyter-notebook
• B. pip install jupyter
• C. pip install notebook
• D. pip install nbconvert

Answer: B

Explanation:
Explanation
Jupyter Notebook is a web-based interactive computational environment.
The command used to install Jupyter Notebook is pip install jupyter.
The command used to start Jupyter Notebook is jupyter notebook.

NEW QUESTION # 49
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