SnowPro Advanced Data Scientist Exam Questions in PDF and Online

QUESTION: 6

Which ones are the key actions in the data collection phase of Machine learning included?

Label
Ingest and Aggregate
Probability
Measure

Answer(s): A,B

Explanation:

The key actions in the data collection phase include:
Label: Labeled data is the raw data that was processed by adding one or more meaningful tags so that a model can learn from it. It will take some work to label it if such information is missing (manually or automatically).
Ingest and Aggregate: Incorporating and combining data from many data sources is part of data collection in AI.
Data collection
Collecting data for training the ML model is the basic step in the machine learning pipeline. The predictions made by ML systems can only be as good as the data on which they have been trained. Following are some of the problems that can arise in data collection:
Inaccurate data. The collected data could be unrelated to the problem statement. Missing data. Sub-data could be missing. That could take the form of empty values in columns or missing images for some class of prediction.
Data imbalance. Some classes or categories in the data may have a disproportionately high or low number of corresponding samples. As a result, they risk being under-represented in the model. Data bias. Depending on how the data, subjects and labels themselves are chosen, the model could propagate inherent biases on gender, politics, age or region, for example. Data bias is difficult to detect and remove.
Several techniques can be applied to address those problems:

Pre-cleaned, freely available datasets. If the problem statement (for example, image classification, object recognition) aligns with a clean, pre-existing, properly formulated dataset, then take ad- vantage of existing, open-source expertise.
Web crawling and scraping. Automated tools, bots and headless browsers can crawl and scrape websites for data.
Private data. ML engineers can create their own data. This is helpful when the amount of data required to train the model is small and the problem statement is too specific to generalize over an open-source dataset.
Custom data. Agencies can create or crowdsource the data for a fee.

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QUESTION: 7

Which ones are the type of visualization used for Data exploration in Data Science?

Heat Maps
Newton AI
Feature Distribution by Class
2D-Density Plots
Sand Visualization

Answer(s): A,D,E

Explanation:

Type of visualization used for exploration:
· Correlation heatmap
· Class distributions by feature
· Two-Dimensional density plots.
All the visualizations are interactive, as is standard for Plotly.
For More details, please refer the below link:
https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41

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QUESTION: 8

Which one is not the feature engineering techniques used in ML data science world?

Imputation
Binning
One hot encoding
Statistical

Answer(s): 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, improve 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:
1. Imputation
Feature engineering deals with inappropriate data, missing values, human interruption, general errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance of the algorithm, and to deal with them "Imputation" technique is used. Imputation is responsible for handling irregularities within the dataset.
For example, removing the missing values from the complete row or complete column by a huge percentage of missing values. But at the same time, to maintain the data size, it is required to impute the missing data, which can be done as:

For numerical data imputation, a default value can be imputed in a column, and missing values can be filled with means or medians of the columns.
For categorical data imputation, missing values can be interchanged with the maximum occurred value in a column.
2. Handling Outliers
Outliers are the deviated values or data points that are observed too away from other data points in such a way that they badly affect the performance of the model. Outliers can be handled with this feature engineering technique. This technique first identifies the outliers and then remove them out. Standard deviation can be used to identify the outliers. For example, each value within a space has a definite to an average distance, but if a value is greater distant than a certain value, it can be considered as an outlier. Z-score can also be used to detect outliers.
3. Log transform
Logarithm transformation or log transform is one of the commonly used mathematical techniques in machine learning. Log transform helps in handling the skewed data, and it makes the distribution more approximate to normal after transformation. It also reduces the effects of outliers on the data, as because of the normalization of magnitude differences, a model becomes much robust.
4. Binning
In machine learning, overfitting is one of the main issues that degrade the performance of the model and which occurs due to a greater number of parameters and noisy data. However, one of the popular techniques of feature engineering, "binning", can be used to normalize the noisy data. This process involves segmenting different features into bins.
5. Feature Split
As the name suggests, feature split is the process of splitting features intimately into two or more parts and performing to make new features. This technique helps the algorithms to better understand and learn the patterns in the dataset.
The feature splitting process enables the new features to be clustered and binned, which results in extracting useful information and improving the performance of the data models.
6. One hot encoding
One hot encoding is the popular encoding technique in machine learning. It is a technique that converts the categorical data in a form so that they can be easily understood by machine learning algorithms and hence can make a good prediction. It enables group the of categorical data without losing any information.

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QUESTION: 9

Skewness of Normal distribution is ___________

Negative
Positive
0
Undefined

Answer(s): 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.

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QUESTION: 10

What is the formula for measuring skewness in a dataset?

MEAN - MEDIAN
MODE - MEDIAN
(3(MEAN - MEDIAN))/ STANDARD DEVIATION
(MEAN - MODE)/ STANDARD DEVIATION

Answer(s): C

Explanation:

Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla- nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

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Snowflake SnowPro Advanced Data Scientist DSA-C03 SnowPro Advanced Data Scientist Exam Questions in PDF

Free Snowflake SnowPro Advanced Data Scientist Dumps Questions (page: 3)

QUESTION: 6

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QUESTION: 7

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QUESTION: 8

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QUESTION: 9

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QUESTION: 10

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