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Snowflake DSA-C02 Practice Test - Questions Answers, Page 5

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Question 41

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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
StreamBI
Streamlit
Streamlit
Streamsets
Streamsets
Rapter
Rapter
Suggested 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.

asked 23/09/2024
Emmanuel Esquivel Guzman
36 questions

Question 42

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Which is the visual depiction of data through the use of graphs, plots, and informational graphics?

Data Interpretation
Data Interpretation
Data Virtualization
Data Virtualization
Data visualization
Data visualization
Data Mining
Data Mining
Suggested answer: D

Explanation:

Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics. Its practitioners use statistics and data science to convey the meaning behind data in ethical and accurate ways.

asked 23/09/2024
Francinilo Leitao Ferreira
34 questions

Question 43

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

Scaler
Scaler
Z-Score
Z-Score
BOXI
BOXI
CMIYC
CMIYC
Suggested answer: B

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 method is 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.

asked 23/09/2024
saharat pinsaran
43 questions

Question 44

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

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.
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.
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.
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.
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)
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)
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.
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.
Suggested answer: C

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()

asked 23/09/2024
Antonio Carlos Figueiredo Junior
50 questions

Question 45

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

Denodo
Denodo
DvSUM
DvSUM
DiYotta
DiYotta
HEX
HEX
Suggested 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

asked 23/09/2024
Chengyang Zhang
43 questions

Question 46

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Which one of the following is not the key component while designing External functions within Snowflake?

Remote Service
Remote Service
API Integration
API Integration
UDF Service
UDF Service
Proxy Service
Proxy Service
Suggested answer: C

Explanation:

What is an External Function?

An external function calls code that is executed outside Snowflake.

The remotely executed code is known as a remote service.

Information sent to a remote service is usually relayed through a proxy service.

Snowflake stores security-related external function information in an API integration.

External Function:

An external function is a type of UDF. Unlike other UDFs, an external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.

Inside Snowflake, the external function is stored as a database object that contains information that Snowflake uses to call the remote service. This stored information includes the URL of the proxy service that relays information to and from the remote service.

Remote Service:

The remotely executed code is known as a remote service.

The remote service must act like a function. For example, it must return a value.

Snowflake supports scalar external functions; the remote service must return exactly one row for each row received.

Proxy Service:

Snowflake does not call a remote service directly. Instead, Snowflake calls a proxy service, which relays the data to the remote service.

The proxy service can increase security by authenticating requests to the remote service.

The proxy service can support subscription-based billing for a remote service. For example, the proxy service can verify that a caller to the remote service is a paid subscriber.

The proxy service also relays the response from the remote service back to Snowflake.

Examples of proxy services include:

Amazon API Gateway.

Microsoft Azure API Management service.

API Integration:

An integration is a Snowflake object that provides an interface between Snowflake and third-party services. An API integration stores information, such as security information, that is needed to work with a proxy service or remote service.

An API integration is created with the CREATE API INTEGRATION command.

Users can write and call their own remote services, or call remote services written by third parties. These remote services can be written using any HTTP server stack, including cloud serverless compute services such as AWS Lambda.

asked 23/09/2024
rita whitfield
38 questions

Question 47

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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 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.
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
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.
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.
Suggested answer: A, B, C, D
asked 23/09/2024
Jonathan McGurgan
34 questions

Question 48

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

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Question 49

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Select the correct mappings:

I) W Weights or Coefficients of independent variables in the Linear regression model --> Model Pa-rameter

II) K in the K-Nearest Neighbour algorithm --> Model Hyperparameter

III) Learning rate for training a neural network --> Model Hyperparameter

IV) Batch Size --> Model Parameter

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Question 50

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

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