Snowflake Snowflake Certifications DSA-C02 Questions & Answers

• Question 1:

  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 anoth- er 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.

  Correct 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 2:

  Which one is not the types of Feature Engineering Transformation?

  A. Scaling

  B. Encoding

  C. Aggregation

  D. Normalization

  Correct Answer: C

  Explanation:

  What is Feature Engineering?

  Feature engineering is the process of transforming raw data into features that are suitable for ma-chine learning models. In other words, it is the process of selecting, extracting, and transforming the most relevant features from the available

  data to build more accurate and efficient machine learning models.

  The success of machine learning models heavily depends on the quality of the features used to train them. Feature engineering involves a set of techniques that enable us to create new features by combining or transforming the existing

  ones. These techniques help to highlight the most important pat-terns and relationships in the data, which in turn helps the machine learning model to learn from the data more effectively.

  What is a Feature?

  In the context of machine learning, a feature (also known as a variable or attribute) is an individual measurable property or characteristic of a data point that is used as input for a machine learning al-gorithm. Features can be numerical,

  categorical, or text-based, and they represent different aspects of the data that are relevant to the problem at hand. For example, in a dataset of housing prices, features could include the number of bedrooms, the square footage, the location,

  and the age of the property. In a dataset of customer demographics, features could include age, gender, income level, and occupation. The choice and quality of features are critical in machine learning, as they can greatly impact the accuracy and performance of the model.

  Why do we Engineer Features?

  We engineer features to improve the performance of machine learning models by providing them with relevant and informative input data. Raw data may contain noise, irrelevant information, or missing values, which can lead to inaccurate or

  biased model predictions. By engineering features, we can extract meaningful information from the raw data, create new variables that capture important patterns and relationships, and transform the data into a more suitable format for

  machine learning algorithms. Feature engineering can also help in addressing issues such as overfitting, underfitting, and high di-mensionality. For example, by reducing the number of features, we can prevent the model from be-coming too

  complex or overfitting to the training data. By selecting the most relevant features, we can improve the model's accuracy and interpretability. In addition, feature engineering is a crucial step in preparing data for analysis and decision- making

  in various fields, such as finance, healthcare, marketing, and social sciences. It can help uncover hidden insights, identify trends and patterns, and support data-driven decision-making.

  We engineer features for various reasons, and some of the main reasons include:

  Improve User Experience: The primary reason we engineer features is to enhance the user experience of a product or service. By adding new features, we can make the product more intuitive, efficient, and user-friendly, which can increase

  user satisfaction and engagement. Competitive Advantage: Another reason we engineer features is to gain a competitive advantage in the marketplace. By offering unique and innovative features, we can differentiate our product from

  competitors and attract more customers. Meet Customer Needs: We engineer features to meet the evolving needs of customers. By analyzing user feedback, market trends, and customer behavior, we can identify areas where new features

  could enhance the product's value and meet customer needs. Increase Revenue: Features can also be engineered to generate more revenue. For example, a new feature that streamlines the checkout process can increase sales, or a feature

  that provides additional functionality could lead to more upsells or cross-sells. Future-Proofing: Engineering features can also be done to future-proof a product or service. By an-ticipating future trends and potential customer needs, we can

  develop features that ensure the product remains relevant and useful in the long term.

  Processes Involved in Feature Engineering

  Feature engineering in Machine learning consists of mainly 5 processes: Feature Creation, Feature Transformation, Feature Extraction, Feature Selection, and Feature Scaling. It is an iterative process that requires experimentation and

  testing to find the best combination of features for a given problem. The success of a machine learning model largely depends on the quality of the features used in the model.

  Feature Transformation

  Feature Transformation is the process of transforming the featuresinto a more suitable representation for the machine learning model. This is done to ensure that the model can effectively learn from the data.

  Types of Feature Transformation:

  Normalization: Rescaling the features to have a similar range, such as between 0 and 1, to prevent some features from dominating others.

  Scaling: Rescaling the features to have a similar scale, such as having a standard deviation of 1, to make sure the model considers all features equally. Encoding: Transforming categorical features into a numerical representation. Examples

  are one-hot encoding and label encoding.

  Transformation: Transforming the features using mathematical operations to change the distribution or scale of the features. Examples are logarithmic, square root, and reciprocal transformations.

• Question 3:

  You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.

  What should you do?

  A. Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.

  B. Create a new version of the dataset using only the new data and retrain the model.

  C. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.

  D. Retrained your training dataset after correcting data outliers and no need to introduce new data.

  Correct Answer: A

  Explanation:

  To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.

  Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.

  These are important metrics to track once models are deployed toproduction. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario,

  retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.

• Question 4:

  Consider a data frame df with columns ['A', 'B', 'C', 'D'] and rows ['r1', 'r2', 'r3']. What does the ex-pression df[lambda x : x.index.str.endswith('3')] do?

  A. Returns the row name r3

  B. Results in Error

  C. Returns the third column

  D. Filters the row labelled r3

  Correct Answer: D

  Explanation:

  It will Filters the row labelled r3.

• Question 5:

  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.

  A. Data Visualization

  B. Data Virtualization

  C. Data Profiling

  D. Data Collection

  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 6:

  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

  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.

• Question 7:

  You are training a binary classification model to support admission approval decisions for a college degree program.

  How can you evaluate if the model is fair, and doesn't discriminate based on ethnicity?

  A. Evaluate each trained model with a validation datasetand use the model with the highest accuracy score.

  B. Remove the ethnicity feature from the training dataset.

  C. Compare disparity between selection rates and performance metrics across ethnicities.

  D. None of the above.

  Correct Answer: C

  Explanation:

  By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.

• Question 8:

  Mark the correct steps for saving the contents of a DataFrame to aSnowflake table as part of Moving Data from Spark to Snowflake?

  A. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the NAME() method. Step 3.Use the dbtable option to specify the table to which data is written. Step 4.Specify the connector options using either the option() or options() method. Step 5.Use the save() method to specify the save mode for the content.

  B. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method. Step 3.Specify the connector options using either the option() or options() method. Step 4.Use the dbtable option to specify the table to which data is written. Step 5.Use the save() method to specify the save mode for the content.

  C. Step 1.Use the write() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method. Step 3.Specify the connector options using either the option() or options() method. Step 4.Use the dbtable option to specify the table to which data is written. Step 5.Use the mode() method to specify the save mode for the content.

  D. Step 1.Use the writer() method of the DataFrame to construct a DataFrameWriter. Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method. Step 3.Use the dbtable option to specify the table to which data is written. Step 4.Specify the connector options using either the option() or options() method. Step 5.Use the save() method to specify the save mode for the content.

  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()

• Question 9:

  Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share us-ing. Which of the following options? Choose 2.

  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.

  Correct Answer: BC

  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 10:

  Which one of the following is not the key component while designing External functions within Snowflake?

  A. Remote Service

  B. API Integration

  C. UDF Service

  D. Proxy Service

  Correct Answer: C

  Explanation:

  What is an External Function?

  An external function calls code that is executed outside Snowflake. The remotely executed code is known as a remote service. Information sent to a remote service is usually relayed through a proxy service. Snowflake stores security-related

  external function information in an API integration.

  External Function:

  An external function is a type of UDF. Unlike other UDFs, an external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.

  Inside Snowflake, the external function is stored as a database object that contains information that Snowflake uses to call the remote service. This stored information includes the URL of the proxy service that relays information to and from

  the remote service.

  Remote Service:

  The remotely executed code is known as a remote service. The remote service must act like a function. For example, it must return a value. Snowflake supports scalar external functions; the remote service must return exactly one row for

  each row received.

  Proxy Service:

  Snowflake does not call a remote service directly. Instead, Snowflake calls a proxy service, which relays the data to the remote service.

  The proxy service can increase security by authenticating requests to the remote service. The proxy service can support subscription-based billing for a remote service. For example, the proxy service can verify that a caller to the remote

  service is a paid subscriber. The proxy service also relays the response from the remote service back to Snowflake.

  Examples of proxy services include:

  Amazon API Gateway.

  Microsoft Azure API Management service.

  API Integration:

  An integration is a Snowflake object that provides an interface between Snowflake and third-party services. An API integration stores information, such as security information, that is needed to work with a proxy service or remote service. An

  API integration is created with the CREATE API INTEGRATION command. Users can write and call their own remote services, or call remote services written by third parties. These remote services can be written using any HTTP server

  stack,including cloud serverless compute services such as AWS Lambda.