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SnowPro Advanced: Data Scientist
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What is a key advantage of using ensemble methods like Random Forest for multiclass classification?
They are faster than individual classifiers
They can handle missing values
They are less complex than single models
ey always outperform single models
wer: B
anation: Random Forest can handle missing values effectively, as it ca urrogate splits to make decisions even when some data points are miss ng it robust for multiclass problems.
When applying PCA, what is the purpose of the covariance matrix?
normalize the data
identify correlations between features create new features
define the target variable wer: B
anation: The covariance matrix is calculated to identify the correlatio
Ans
Expl ns
between features. PCA uses this matrix to determine the directions (principal components) that maximize variance in the data.
Which of the following is a key aspect of compliance considerations in data
science?
Lack of data documentation
Data encryption and access controls
Open access to all data
anation: Compliance considerations emphasize the importance of data yption and access controls to protect sensitive information and adhere
rotection regulations, ensuring data privacy and security.
ch of the following is NOT a benefit of using Snowflake for Data Scie ines?
utomatic scaling of resources igh concurrency
imited data storage options implified data sharing
wer: C
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encr to
data p
Whi nce
Pipel
A
H
L
S
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Explanation: Limited data storage options is NOT a benefit of using Snowflake. In fact, Snowflake provides extensive storage options and capabilities, making it ideal for handling large volumes of data in Data Science Pipelines.
Which technique can be used to handle imbalanced datasets in classification tasks?
Data Augmentation
Cross-Validation
Under-sampling and Over-sampling
wer: C
anation: Under-sampling reduces the number of instances from the rity class, while over-sampling increases instances in the minority cla iming to balance class distributions for more effective model trainin
does the term "autocorrelation" refer to in regression analysis?
he correlation between two independent variables he correlation of residuals at different times
he relationship between predicted and actual values he effect of outliers on regression coefficients
wer: B
Expl
majo ss,
both a g.
What
T
T
T
T
Ans
Explanation: Autocorrelation refers to the correlation of residuals (errors) at different points in time. This is a concern in time series data and can violate the assumption of independence of errors in regression analysis.
In the context of time series forecasting, what does "seasonality" refer to?
Long-term trends in the data
Random fluctuations
Regular patterns that repeat over a known period
anation: Seasonality refers to regular, predictable patterns that occur a fic intervals in time series data, such as daily, monthly, or yearly uations.
ch of the following statements is true about PCA?
CA only works with categorical data.
CA transforms data to a new coordinate system. CA can only reduce dimensions to two.
CA is a supervised learning technique. wer: B
anation: PCA transforms data into a new coordinate system where the
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speci fluct
Whi
P
P
P
P
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greatest variance by any projection lies on the first coordinate (principal component), followed by the second greatest variance on the second coordinate, and so forth.
What is a key advantage of the Snowflake Marketplace for data scientists?
Limited access to datasets.
Centralized access to diverse datasets.
High costs associated with data procurement.
anation:
y advantage of the Snowflake Marketplace for data scientists is centra ss to a diverse range of datasets. This accessibility enables data scienti elevant data quickly and efficiently, enhancing their analytical
bilities.
ch function would you use to convert a date to a string in a specific for nowflake?
O_DATE() O_STRING() O_CHAR() ORMAT_DATE()
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A ke lized
acce sts to
find r capa
Whi mat
in S
T
T
T
F
Answer: C
Explanation: The TO_CHAR() function is used to convert a date or timestamp to a string in a specified format, allowing for flexible formatting of date outputs in queries.
Which of the following techniques can be used for dimensionality reduction?
K-means clustering
Linear Regression
Principal Component Analysis (PCA)
wer: C anation:
cipal Component Analysis (PCA) is a widely used technique for nsionality reduction, transforming a high-dimensional dataset into a l nsional one while preserving as much variance as possible. This helps lify models and reduce overfitting.
ch command is used to delete a share in Snowflake?
ROP SHARE ELETE SHARE EMOVE SHARE HARE DROP
Expl Prin
dime ower-
dime simp
Whi
D
D
R
S
Answer: A
Explanation: The command DROP SHARE is used to delete a share in Snowflake, removing the access granted to the shared objects and preventing further access by the specified accounts.
To update the "status" column of the "orders" table to "shipped" where the order ID is 123, which command should you use?
UPDATE orders SET status = 'shipped' WHERE order_id = 123;
HANGE orders SET status = 'shipped' WHERE order_id = 123; ET orders.status = 'shipped' WHERE order_id = 123;
wer: A
anation: The command UPDATE orders SET status = 'shipped' WHE r_id = 123; is the correct way to update a specific record in SQL. The ns do not follow standard SQL syntax.
ulticlass classification, what does "stratified sampling" ensure?
qual representation of all classes in training and test sets andom selection of samples without consideration of class election of only the majority class
limination of minority classes
C
S
Ans
Expl RE
orde other
optio
In m
E
R
S
E
Answer: A
Explanation: Stratified sampling ensures that each class is represented in the training and test sets in proportion to its occurrence in the entire dataset, which helps maintain class distribution and improves model performance.
What is the effect of including an irrelevant variable in a regression model concerning multicollinearity?
It reduces bias
can increase multicollinearity simplifies the model
wer: C
anation: Including irrelevant variables can introduce additional correla ng predictors, potentially increasing multicollinearity and complicatin el.
can you show the current user s role?
HOW CURRENT ROLE; ELECT CURRENT_ROLE(); ET CURRENT ROLE; ISPLAY ROLE;
It
It
Ans
Expl tions
amo g the
mod
How
S
S
G
D
Answer: B
Explanation: The command SELECT CURRENT_ROLE(); retrieves the role currently assigned to the user. This is the proper way to check the active role in SQL.
What is the default retention period for Time Travel in Snowflake if not explicitly configured?
1 day
days days
wer: C
anation: If not explicitly configured, the default retention period for T el in Snowflake is 30 days, allowing users to access historical data ch within this time frame.
type of data preprocessing is typically performed before generating a
ata augmentation ata encoding
ata imputation
30
90
Ans
Expl ime
Trav anges
made
What heat
map?
D
D
D
Data transformation Answer: C
Explanation:
Data imputation is often necessary before generating a heat map to handle missing values, ensuring that the dataset used for visualization is complete and
accurate. While data encoding (B) and transformation (D) are also important, imputation specifically addresses missing data issues.