Mark the Incorrect understanding of Data Scientist about Streams?
Correct Answer: B,D
Explanation Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views. stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, several hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables. Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed. The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise it stays at the same position.
Question 12
Which of the following process best covers all of the following characteristics? 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 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.
Question 13
Mark the Incorrect statements regarding MIN / MAX Functions?
Correct Answer: B
Explanation NULL values are ignored unless all the records are NULL, in which case a NULL value is returned
Question 14
Which one is not the feature engineering techniques used in ML data science world?
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.
Question 15
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Provider?
Correct 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 offer-ings with many consumers simultaneously, rather than maintain sharing relationships with each indi-vidual 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 ac-cess 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 Mi-crosoft Azure Government. Support for Microsoft Azure Government is planned.
