Correct Answer: C

Explanation:

Data processing and analysis cannot happen without data profiling--reviewing source data for con-tent and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.

What is data profiling?

Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.

Data profiling is a crucial part of:

Data warehouse and business intelligence (DW/BI) projects--dataprofiling can uncover data quality issues in data sources, and what needs to be corrected in ETL.

Data conversion and migration projects--data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also un-cover new requirements for the target system.

Source system data quality projects--data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).

Data profiling involves:

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.

Correct Answer: 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.

Correct 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()

Correct 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, 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 acertain 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 noisydata. 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 theof categorical data without losing any information.

Correct Answer: AC

Explanation:

Query and process data with a DataFrame object. Refer to Working with DataFrames in Snowpark Python.

Convert custom lambdas and functions to user-defined functions(UDFs) that you can call to process data.

Write a user-defined tabular function (UDTF) that processes data and returns data in a set of rows with one or more columns.

Write a stored procedure that you can call to process data, or automate with a task to build a data pipeline.

Correct 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

Correct Answer: C

Explanation:

Computes length of column A and Sum of Column B values of each group

Correct Answer: ABCD

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.

Correct 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 therow 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

Correct Answer: C

Explanation:

Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.