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Question: 145
Which ones are the type of visualization used for Data exploration in Data Science?
A. Heat Maps
B. Newton AI
C. Feature Distribution by Class
D. 2D-Density Plots
E. Sand Visualization
wrong
Answer: A
Explanation:
For data exploration in Data Science, visualizations are used to understand the underlying patterns, relationships,
anomalies, and distributions in the data. Among the given options:
A. Heat Maps - Correct. Heat maps are used to represent data values using colors. They can visualize the distribution
of values across two dimensions, making them useful for understanding correlations or patterns.
C. Feature Distribution by Class - Correct. This visualization displays the distribution of a feature (or multiple features)
for different classes. It can be helpful in classification problems to understand how features vary across classes.
D. 2D-Density Plots - Correct. These plots are a way to represent the distribution of two continuous variables, usually
with contour lines or color-coded regions. It helps in visualizing where data points are concentrated.
B. Newton AI - Incorrect. Newton AI is not a type of visualization. It might refer to an AI platform or product, but it's
not a visualization method used in data exploration.
E. Sand Visualization - Incorrect. This isn't a recognized standard type of visualization in Data Science.
So, the correct answers are A, C, and D.
Question: 146
Which one is not the feature engineering techniques used in ML data science world?
A. Imputation
B. Binning
C. One hot encoding
D. Statistical
Answer: D
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, Boost 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:
Question: 147
Skewness of Normal distribution is ___________
A. Negative
B. Positive
C. 0
D. Undefined
Answer: C
Explanation:
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical explanation for
mathematical proofs, you can refer to books or websites that speak on the same in detail.
Question: 148
What is the formula for measuring skewness in a dataset?
A. MEAN - MEDIAN
B. MODE - MEDIAN
C. (3(MEAN - MEDIAN))/ STANDARD DEVIATION
D. (MEAN - MODE)/ STANDARD DEVIATION
Answer: C
Explanation:
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical explanation for
mathematical proofs, you can refer to books or websites that speak on the same in detail.
Question: 149
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Provider?
A. Publish listings for free-to-use datasets to generate interest and new opportunities among the Snowflake customer
base.
B. Publish listings for datasets that can be customized for the consumer.
C. Share live datasets securely and in real-time without creating copies of the data or im-posing data integration tasks
on the consumer.
D. Eliminate the costs of building and maintaining APIs and data pipelines to deliver data to customers.
Answer: A,B,C,D
Explanation:
All are correct!
About the Snowflake Marketplace
You can use the Snowflake Marketplace to discover and access third-party data and services, as well as market your
own data products across the Snowflake Data Cloud.
As a data provider, you can use listings on the Snowflake Marketplace to share curated data offerings with many
consumers simultaneously, rather than maintain sharing relationships with each individual consumer. With Paid
Listings, you can also charge for your data products.
As a consumer, you might use the data provided on the Snowflake Marketplace to explore and access the following:
Historical data for research, forecasting, and machine learning.
Up-to-date streaming data, such as current weather and traffic conditions.
Specialized identity data for understanding subscribers and audience targets.
New insights from unexpected sources of data.
The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on Amazon Web Services,
Google Cloud Platform, and Microsoft Azure, with the exception of Microsoft Azure Government. Support for
Microsoft Azure Government is planned.
Question: 150
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
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.
Answer: A,B,C,D
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: 151
Which one is the incorrect option to share data in Snowflake?
A. a Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
B. a Direct Marketplace, in which you directly share specific database objects (a share) to another account in your
region using Snowflake Marketplace.
C. a Direct Share, in which you directly share specific database objects (a share) to another account in your region.
D. a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
Answer: B
Explanation:
Options for Sharing in Snowflake
You can share data in Snowflake using one of the following options:
� a Listing, in which you offer a share and additional metadata as a data product to one or more ac-counts,
� a Direct Share, in which you directly share specific database objects (a share) to another account in your region,
� a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
Question: 152
Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share using.
Which of the following options?
A. Grant privileges on objects to a share via Account role.
B. Grant privileges on objects directly to a share.
C. Grant privileges on objects to a share via a database role.
D. Grant privileges on objects to a share via a third-party role.
Answer: A,B,C
Explanation:
What 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.
Question: 153
Secure Data Sharing do not let you share which of the following selected objects in a database in your account with
other Snowflake accounts?
A. Sequences
B. Tables
C. External tables
D. Secure UDFs
Answer: A
Explanation:
Secure Data Sharing lets you share selected objects in a database in your account with other Snow-flake accounts.
You can share the following Snowflake database objects:
Tables
External tables
Secure views
Secure materialized views
Secure UDFs
Snowflake enables the sharing of databases through shares, which are created by data providers and �imported� by data
consumers.
Question: 154
Which one is incorrect understanding about Providers of Direct share?
A. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts
to consume.
B. As a data provider, you share a database with one or more Snowflake accounts.
C. You can create as many shares as you want, and add as many accounts to a share as you want.
D. If you want to provide a share to many accounts, you can do the same via Direct Share.
Answer: D
Explanation:
If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.
Question: 155
As Data Scientist looking out to use Reader account, Which ones are the correct considerations about Reader Accounts
for Third-Party Access?
A. Reader accounts (formerly known as �read-only accounts�) provide a quick, easy, and cost-effective way to share
data without requiring the consumer to become a Snowflake customer.
B. Each reader account belongs to the provider account that created it.
C. Users in a reader account can query data that has been shared with the reader account, but cannot perform any of
the DML tasks that are allowed in a full account, such as data loading, insert, update, and similar data manipulation
operations.
D. Data sharing is only possible between Snowflake accounts.
wrong
Answer: D
Explanation:
Let's evaluate each of the statements regarding Reader Accounts for Third-Party Access in Snowflake:
A. Correct. Reader accounts (formerly known as � read-only accounts � ) are specifically designed for sharing data
with third-party consumers without requiring them to be Snowflake customers. They allow consumers to query shared
data sets.
B. Correct. Each reader account is indeed linked to the provider account that created it. The provider manages and
incurs the costs of the reader account.
C. Correct. Users in a reader account can only query the shared data. They don't have permissions to perform DML
operations (like insert, update, delete). Their primary purpose is to read and analyze the shared data.
D. Incorrect. While it's true that typical data sharing in Snowflake occurs between Snowflake accounts, the concept of
the reader account was introduced specifically to share data with third parties that do not have a Snowflake account.
So, the correct considerations about Reader Accounts for Third-Party Access are options A, B, and C.
Question: 156
SHOW GRANTS OF SHARE product_s;
A. GRANT USAGE ON DATABASE product_db TO SHARE product_s;
B. CREATE DIRECT SHARE product_s;
C. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
D. ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
Answer: C
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
Question: 157
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
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 the
row 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: 158
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data
science Pipelines & helps in transforming only the New/Modified data only?
A. METADATA$ACTION
B. METADATA$FILE_ID
C. METADATA$ISUPDATE
D. METADATA$DELETE
E. METADATA$ROW_ID
Answer: A,C,E
Explanation:
A stream stores an offset for the source object and not any genuine table columns or data. When que-ried, a stream
accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering)
with the following additional columns: METADATA$ACTION
Indicates the DML operation (INSERT, DELETE) recorded.
METADATA$ISUPDATE
Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are
represented as a pair of DELETE and INSERT records in the stream with a metadata column
METADATA$ISUPDATE values set to TRUE.
Note that streams record the differences between two offsets. If a row is added and then updated in the current offset,
the delta change is a new row. The METADATA$ISUPDATE row records a FALSE
value.
METADATA$ROW_ID
Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.
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