Snowflake DSA-C02 Online Practice Questions and Exam Preparation

Snowflake DSA-C02 Online Questions & Answers

• Question 21:

  Performance metrics are a part of every machine learning pipeline, Which ones are not the performance metrics used in the Machine learning?

  A. R - (R-Squared)
  B. Root Mean Squared Error (RMSE)
  C. AU-ROC
  D. AUM
  D. AUM

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  Explanation/Reference:

• Question 22:

  Data Scientist can query, process, and transform data in a which of the following ways using Snowpark Python. Choose 2.

  A. Query and process data with a DataFrame object.
  B. Write a user-defined tabular function (UDTF) that processes data and returns data in a set of rows with one or more columns.
  C. SnowPark currently do not support writing UDTF.
  D. Transform Data using DataIKY tool with SnowPark API.
  A. Query and process data with a DataFrame object.
  C. SnowPark currently do not support writing UDTF.

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  Explanation/Reference:

  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.

• Question 23:

  Select the Correct Statements regarding Normalization? Choose 2.

  A. Normalization technique uses minimum and max values for scaling of model.
  B. Normalization technique uses mean and standard deviation for scaling of model.
  C. Scikit-Learn provides a transformer RecommendedScaler for Normalization.
  D. Normalization got affected by outliers.
  A. Normalization technique uses minimum and max values for scaling of model.
  D. Normalization got affected by outliers.

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  Explanation/Reference:

  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.

• Question 24:

  Which command is used to install Jupyter Notebook?

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

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  Explanation/Reference:

  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.

• Question 25:

  Which ones are the known limitations of using External function? Choose all apply.

  A. Currently, external functions cannot be shared with data consumers via Secure Data Sharing.
  B. Currently, external functions must be scalar functions. A scalar external function re-turns a single value for each input row.
  C. External functions have more overhead than internal functions (both built-in functions and internal UDFs) and usually execute more slowly
  D. An external function accessed through an AWS API Gateway private endpoint can be accessed only from a Snowflake VPC (Virtual Private Cloud) on AWS and in the same AWS region.
  A. Currently, external functions cannot be shared with data consumers via Secure Data Sharing.
  B. Currently, external functions must be scalar functions. A scalar external function re-turns a single value for each input row.
  C. External functions have more overhead than internal functions (both built-in functions and internal UDFs) and usually execute more slowly
  D. An external function accessed through an AWS API Gateway private endpoint can be accessed only from a Snowflake VPC (Virtual Private Cloud) on AWS and in the same AWS region.

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  Explanation/Reference:

• Question 26:

  What is the risk with tuning hyper-parameters using a test dataset?

  A. Model will overfit the test set
  B. Model will underfit the test set
  C. Model will overfit the training set
  D. Model will perform balanced
  A. Model will overfit the test set

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  Explanation/Reference:

  The model will not generalize well to unseen data because it overfits the test set. Tuning model hyper-parameters to a test set means that the hyper-parameters may overfit to that test set. If the same test set is used to estimate performance,

  it will produce an overestimate. The test set should be used only for testing, not for parameter tuning. Using a separate validation set for tuning and test set for measuring performance provides unbiased, realistic measurement of

  performance.

  What are hyper-parameters?

  Hyper-parameters are parameters whose values control the learning process and determine the values of model parameters that a learning algorithm ends up learning. We can't calculate their values from the data.

  Example: Number of clusters in clustering, number of hidden layers in a neural network, and depth of a tree are some of the examples of hyper-parameters.

  What is the hyper-parameter tuning?

  Hyper-parameter tuning is the process of choosing the right combination of hyper- parameters that maximizes the model performance. It works by running multiple trials in a single training process. Each trial is a complete execution of your

  training application with values for your chosen hyper-parameters, set within the limits you specify. This process once finished will give you the set of hyper-parameter values that are best suited for the model to give optimal results.

• Question 27:

  All Snowpark ML modeling and preprocessing classes are in the ________ namespace?

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

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  Explanation/Reference:

  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.

• Question 28:

  Which method is used for detecting data outliers in Machine learning?

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

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  Explanation/Reference:

  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.

• Question 29:

  The most widely used metrics and tools to assess a classification model are:

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

• Question 30:

  Which metric is not used for evaluating classification models?

  A. Recall
  B. Accuracy
  C. Mean absolute error
  D. Precision
  C. Mean absolute error

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  Explanation/Reference:

  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.