2023 Provide Updated Snowflake DSA-C02 Dumps as Practice Test and PDF [Q35-Q56]

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2023 Provide Updated Snowflake DSA-C02 Dumps as Practice Test and PDF

DSA-C02 Dumps are Available for Instant Access

QUESTION 35
Which of the following process best covers all of the following characteristics?
Collecting descriptive statistics like min, max, count and sum.
Collecting data types, length and recurring patterns.
Tagging data with keywords, descriptions or categories.
Performing data quality assessment, risk of performing joins on the data.
Discovering metadata and assessing its accuracy.
Identifying distributions, key candidates, foreign-key candidates,functional dependencies, embedded value dependencies, and performing inter-table analysis.

Data Visualization

Data Virtualization

Data Profiling

Data Collection

QUESTION 36
Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?

k-fold cross-validation

Leave-one-out cross-validation

Holdout method

All of the above

QUESTION 37
Mark the incorrect statement regarding Python UDF?

Python UDFs can contain both new code and calls to existing packages

For each row passed to a UDF, the UDF returns either a scalar (i.e. single) value or, if defined as a table function, a set of rows.

A UDF also gives you a way to encapsulate functionality so that you can call it repeatedly from multiple places in code

A scalar function (UDF) returns a tabular value for each input row

QUESTION 38
Select the Correct Statements regarding Normalization?

Normalization technique uses minimum and max values for scaling of model.

Normalization technique uses mean and standard deviation for scaling of model.

Scikit-Learn provides a transformer RecommendedScaler for Normalization.

Normalization got affected by outliers.

QUESTION 39
What is the formula for measuring skewness in a dataset?

MEAN – MEDIAN

MODE – MEDIAN

(3(MEAN – MEDIAN))/ STANDARD DEVIATION

(MEAN – MODE)/ STANDARD DEVIATION

QUESTION 40
The most widely used metrics and tools to assess a classification model are:

Confusion matrix

Cost-sensitive accuracy

Area under the ROC curve

All of the above

QUESTION 41
Which ones are the correct rules while using a data science model created via External function in Snowflake?

External functions return a value. The returned value can be a compound value, such as a VARIANT that contains JSON.

External functions can be overloaded.

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

External functions can accept Model parameters.

QUESTION 42
Which ones are the known limitations of using External function?

Currently, external functions cannot be shared with data consumers via Secure Data Sharing.

Currently, external functions must be scalar functions. A scalar external function re-turns a single value for each input row.

External functions have more overhead than internal functions (both built-in functions and internal UDFs) and usually execute more slowly

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.

QUESTION 43
How do you handle missing or corrupted data in a dataset?

Drop missing rows or columns

Replace missing values with mean/median/mode

Assign a unique category to missing values

All of the above

QUESTION 44
In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?

by 1

no change

by intercept

by its slope

QUESTION 45
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?

Evaluate each trained model with a validation datasetand use the model with the highest accuracy score.

Remove the ethnicity feature from the training dataset.

Compare disparity between selection rates and performance metrics across ethnicities.

None of the above.

QUESTION 46
Which of the following Snowflake parameter can be used to Automatically Suspend Tasks which are running Data science pipelines after specified Failed Runs?

SUSPEND_TASK

SUSPEND_TASK_AUTO_NUM_FAILURES

SUSPEND_TASK_AFTER_NUM_FAILURES

There is none as such available.

QUESTION 47
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?

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.

Create a new version of the dataset using only the new data and retrain the model.

Retrained your training dataset after correcting data outliers & no need to introduce new data.

Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.

QUESTION 48
Data Scientist used streams in ELT (extract, load, transform) processes where new data inserted in-to a staging table is tracked by a stream. A set of SQL statements transform and insert the stream contents into a set of production tables. Raw data is coming in the JSON format, but for analysis he needs to transform it into relational columns in the production tables. which of the following Data transformation SQL function he can used to achieve the same?

He could not apply Transformation on Stream table data.

lateral flatten()

METADATA$ACTION ()

Transpose()

QUESTION 49
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;

GRANT USAGE ON DATABASE product_db TO SHARE product_s;

CREATE DIRECT SHARE product_s;

GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;

ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;

QUESTION 50
Which one is not Types of Feature Scaling?

Economy Scaling

Min-Max Scaling

Standard Scaling

Robust Scaling

QUESTION 51
Which of the following is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way?

StreamBI

Streamlit

Streamsets

Rapter

QUESTION 52
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?

Groups df based on index values

Groups df based on length of each index value

Groups df based on index strings

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

QUESTION 53
Which 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?

RUN TASK

CALL TASK

EXECUTE TASK

RUN ROOT TASK

QUESTION 54
Which of the following method is used for multiclass classification?

one vs rest

loocv

all vs one

one vs another

Explanation
Binary vs. Multi-Class Classification
Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.
As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data.
The technique enables developers to categorize the test data into multiple binary class labels.
That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.
One-Vs-Rest Classification Model for Multi-Class Classification
Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.
For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:
Problem one: red vs. green/blue
Problem two: blue vs. green/red
Problem three: green vs. blue/red
The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.
The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used forclassification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.

QUESTION 55
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?

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.

Create a new version of the dataset using only the new data and retrain the model.

Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.

Retrained your training dataset after correcting data outliers & no need to introduce new data.

QUESTION 56
Mark the Incorrect understanding of Data Scientist about Streams?

Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views.

Streams can track changes in materialized views.

Streams itself does not contain any table data.

Streams do not support repeatable read isolation.