Snowflake Snowflake Certifications DSA-C02 Questions & Answers

• Question 41:

  Which of the following is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way?

  A. StreamBI

  B. Streamlit

  C. Streamsets

  D. Rapter

  Correct Answer: B

  Explanation:

  Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML)

  visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.

  Why Streamlit?

  Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development

  time from days to hours. Following are some reasons to choose the Streamlit:

  It is a free and open-source library.

  Installing Streamlit is as simple as installing any other python package It is easy to learn because you won't need any web development experience, only a basic under-standing of Python is enough to build a data application. It is compatible

  with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.

• Question 42:

  Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?

  A. k-fold cross-validation

  B. Leave-one-out cross-validation

  C. Holdout method

  D. All of the above

  Correct Answer: B

  Explanation:

  Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.

  Cross validation

  It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by

  the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.

  Leave-one-out cross validation

  Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one

  point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.

• Question 43:

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

  A. REPLACE_TO_PANDAS

  B. SNOWPARK_TO_PANDAS

  C. CONVERT_TO_PANDAS

  D. TO_PANDAS

  Correct Answer: D

  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()

• Question 44:

  Select the Data Science Tools which are known to provide native connectivity to Snowflake?

  A. Denodo

  B. DvSUM

  C. DiYotta

  D. HEX

  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

• Question 45:

  What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer? Choose all apply.

  A. Discover and test third-party data sources.

  B. Receive frictionless access to raw data products from vendors.

  C. Combine new datasets with your existing data in Snowflake to derive new business in- sights.

  D. Use the business intelligence (BI)/ML/Deep learning tools of her choice.

  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.

• Question 46:

  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

  Correct Answer: A

  Explanation:

  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 47:

  Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10']. What does the expression g = df.groupby(df.index.str.len()) do?

  A. Groups df based on index values

  B. Groups df based on length of each index value

  C. Groups df based on index strings

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

  Correct Answer: D

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

• Question 48:

  Which metric is not used for evaluating classification models?

  A. Recall

  B. Accuracy

  C. Mean absolute error

  D. Precision

  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.

• Question 49:

  Which object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data of Data Science Pipelines?

  A. Task

  B. Dynamic tables

  C. Stream

  D. Tags

  E. Delta

  F. OFFSET

  Correct Answer: C

  Explanation: A stream object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change,so that actions can be taken using the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at therow level, between two transactional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion. Streams can be created to query change data on the following objects: Standard tables, including shared tables. Views, including secure views Directory tables Event tables

• Question 50:

  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. by 1

  B. no change

  C. by intercept

  D. by its slope

  Correct Answer: D

  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.