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NEW QUESTION # 15
Which one is the incorrect option to share data in Snowflake?
- A. a Direct Marketplace, in which you directly share specific database objects (a share) to another account in your region using Snowflake Marketplace.
- B. a Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
- 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.
Answer: A
Explanation:
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.
NEW QUESTION # 16
Which of the following is a useful tool for gaining insights into the relationship between features and predictions?
- A. numpy plots
- B. Partial dependence plots(PDP)
- C. sklearn plots
- D. FULL dependence plots (FDP)
Answer: B
Explanation:
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.
NEW QUESTION # 17
Which one is not the feature engineering techniques used in ML data science world?
- A. Statistical
- B. Binning
- C. Imputation
- D. One hot encoding
Answer: A
Explanation:
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.
NEW QUESTION # 18
Which command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task?
- A. RUN ROOT TASK
- B. EXECUTE TASK
- C. CALL TASK
- D. RUN TASK
Answer: B
Explanation:
Explanation
The EXECUTE TASK command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task. A successful run of a roottask triggers a cascading run of child tasks in the DAG as their precedent task completes, as though the root task had run on its defined schedule.
This SQL command is useful for testing new or modified standalone tasks and DAGs before you enable them to execute SQL code in production.
Call this SQL command directly in scripts or in stored procedures. In addition, this command sup-ports integrating tasks in external data pipelines. Any third-party services that can authenticate into your Snowflake account and authorize SQL actions can execute the EXECUTE TASK command to run tasks.
NEW QUESTION # 19
Which of the following is a common evaluation metric for binary classification?
- A. Area under the ROC curve (AUC)
- B. Accuracy
- C. Mean squared error (MSE)
- D. F1 score
Answer: A
Explanation:
Explanation
The area under the ROC curve (AUC) is a common evaluation metric for binary classification, which measures the performance of a classifier at different threshold values for the predicted probabilities. Other common metrics include accuracy, precision, recall, and F1 score, which are based on the confusion matrix of true positives, false positives, true negatives, and false negatives.
NEW QUESTION # 20
Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?
- A. All of the above
- B. Holdout method
- C. k-fold cross-validation
- D. Leave-one-out cross-validation
Answer: D
Explanation:
Explanation
Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.
Cross validation
It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.
Leave-one-out cross validation
Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.
NEW QUESTION # 21
Which of the following is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way?
- A. Rapter
- B. StreamBI
- C. Streamsets
- D. Streamlit
Answer: D
Explanation:
Explanation
Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML) visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.
Why Streamlit?
Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development time from days to hours. Following are some reasons to choose the Streamlit:
It is a free and open-source library.
Installing Streamlit is as simple as installing any other python package It is easy to learn because you won't need any web development experience, only a basic under-standing of Python is enough to build a data application.
It is compatible with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.
NEW QUESTION # 22
Which of the learning methodology applies conditional probability of all the variables with respec-tive the dependent variable?
- A. Artificial learning
- B. Reinforcement learning
- C. Unsupervised learning
- D. Supervised learning
Answer: B
Explanation:
Explanation
Supervised learning methodology applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few random experiments.
Conditional probability is thus the likelihood of an event or outcome occurring based on the occurrence of some other event or prior outcome. Two events are said tobe independent if one event occurring does not affect the probability that the other event will occur.
NEW QUESTION # 23
Select the correct mappings:
I. W Weights or Coefficients of independent variables in the Linear regression model --> Model Pa-rameter II. K in the K-Nearest Neighbour algorithm --> Model Hyperparameter III. Learning rate for training a neural network --> Model Hyperparameter IV. Batch Size --> Model Parameter
- A. I,II
- B. II,III,IV
- C. III,IV
- D. I,II,III
Answer: D
Explanation:
Explanation
Hyperparameters in Machine learning are those parameters that are explicitly defined by the user to control the learning process. These hyperparameters are used to improve the learning of the model, and their values are set before starting the learning process of the model.
What are hyperparameters?
In Machine Learning/Deep Learning, a model is represented by its parameters. In contrast, a training process involves selecting the best/optimal hyperparameters that are used by learning algorithms to provide the best result. So, what are these hyperparameters? The answer is, "Hyperparameters are defined as the parameters that are explicitly defined by the user to control the learning process." Here the prefix "hyper" suggests that the parameters are top-level parameters that are used in con-trolling the learning process. The value of the Hyperparameter is selected and set by the machine learning engineer before the learning algorithm begins training the model. Hence, these are external to the model, and their values cannot be changed during the training process.
Some examples of Hyperparameters in Machine Learning
The k in kNN or K-Nearest Neighbour algorithm
Learning rate for training a neural network
Train-test split ratio
Batch Size
Number of Epochs
Branches in Decision Tree
Number of clusters in Clustering Algorithm
Model Parameters:
Model parameters are configuration variables that are internal to the model, and a model learns them on its own. For example, W Weights or Coefficients of independentvariables in the Linear regression model. or Weights or Coefficients of independent variables in SVM, weight, and biases of a neural network, cluster centroid in clustering. Some key points for model parameters are as follows:
They are used by the model for making predictions.
They are learned by the model from the data itself
These are usually not set manually.
These are the part of the model and key to a machine learning Algorithm.
Model Hyperparameters:
Hyperparameters are those parameters that are explicitly defined by the user to control the learning process.
Some key points for model parameters are as follows:
These are usually defined manually by the machine learning engineer.
One cannot know the exact best value for hyperparameters for the given problem. The best value can be determined either by the rule of thumb or by trial and error.
Some examples of Hyperparameters are the learning rate for training a neural network, K in the KNN algorithm.
NEW QUESTION # 24
Mark the incorrect statement regarding usage of Snowflake Stream & Tasks?
- A. Snowflake automatically resizes and scales the compute resources for serverless tasks.
- B. Snowflake ensures only one instance of a task with a schedule (i.e. a standalone task or the root task in a DAG) is executed at a given time. If a task is still running when the next scheduled execution time occurs, then that scheduled time is skipped.
- C. Streams support repeatable read isolation.
- D. An standard-only stream tracks row inserts only.
Answer: D
Explanation:
Explanation
All are correct except a standard-only stream tracks row inserts only.
A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, up-dates, and deletes (including table truncates).
NEW QUESTION # 25
Which of the following cross validation versions is suitable quicker cross-validation for very large datasets with hundreds of thousands of samples?
- A. All of the above
- B. Leave-one-out cross-validation
- C. k-fold cross-validation
- D. Holdout method
Answer: D
Explanation:
Explanation
Holdout cross-validation method is suitable for very large dataset because it is the simplest and quicker to compute version of cross-validation.
Holdout method
In this method, the dataset is divided into two sets namely the training and the test set with the basic property that the training set is bigger than the test set. Later, the model is trained on the training dataset and evaluated using the test dataset.
NEW QUESTION # 26
Select the Data Science Tools which are known to provide native connectivity to Snowflake?
- A. Denodo
- B. DvSUM
- C. DiYotta
- D. HEX
Answer: D
Explanation:
Explanation
Hex - collaborative data science and analytics platform
Denodo - data virtualization and federation platform
DvSum - data catalog and data intelligence platform
Diyotta - data integration and migration
NEW QUESTION # 27
Which ones are the correct rules while using a data science model created via External function in Snowflake?
- A. External functions can accept Model parameters.
- B. An external function can appear in any clause of a SQL statement in which other types of UDF can appear.
- C. External functions return a value. The returned value can be a compound value, such as a VARIANT that contains JSON.
- D. External functions can be overloaded.
Answer: A,B,C,D
Explanation:
Explanation
From the perspective of a user running a SQL statement, an external function behaves like any other UDF .
External functions follow these rules:
External functions return a value.
External functions can accept parameters.
An external function can appear in any clause of a SQL statement in which other types of UDF can appear. For example:
1.select my_external_function_2(column_1, column_2)
2.from table_1;
1.select col1
2.from table_1
3.where my_external_function_3(col2) < 0;
1.create view view1 (col1) as
2.select my_external_function_5(col1)
3.from table9;
An external function can be part of a more complex expression:
1.select upper(zipcode_to_city_external_function(zipcode))
2.from address_table;
The returned value can be a compound value, such as a VARIANT that contains JSON.
External functions can be overloaded; two different functions can have the same name but different signatures (different numbers or data types of input parameters).
NEW QUESTION # 28
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. Retrained your training dataset after correcting data outliers & no need to introduce new data.
- C. Create a new version of the dataset using only the new data and retrain the model.
- D. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
Answer: A
Explanation:
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.
NEW QUESTION # 29
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?
- A. Data Virtualization
- B. Data Mining
- C. Data Interpretation
- D. Data visualization
Answer: B
Explanation:
Explanation
Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics.
Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.
NEW QUESTION # 30
How do you handle missing or corrupted data in a dataset?
- A. Assign a unique category to missing values
- B. Replace missing values with mean/median/mode
- C. All of the above
- D. Drop missing rows or columns
Answer: C
NEW QUESTION # 31
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. Retrained your training dataset after correcting data outliers & no need to introduce new data.
- C. Create a new version of the dataset using only the new data and retrain the model.
- D. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
Answer: A
Explanation:
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.
NEW QUESTION # 32
Which one is incorrect understanding about Providers of Direct share?
- A. You can create as many shares as you want, and add as many accounts to a share as you want.
- B. If you want to provide a share to many accounts, you can do the same via Direct Share.
- C. As a data provider, you share a database with one or more Snowflake accounts.
- D. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts to consume.
Answer: B
Explanation:
Explanation
If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.
NEW QUESTION # 33
Which ones are the known limitations of using External function?
- A. An external function accessed through an AWS API Gateway private endpoint can be accessed only from a Snowflake VPC (Virtual Private Cloud) on AWS and in the same AWS region.
- B. Currently, external functions must be scalar functions. A scalar external function re-turns a single value for each input row.
- C. Currently, external functions cannot be shared with data consumers via Secure Data Sharing.
- D. External functions have more overhead than internal functions (both built-in functions and internal UDFs) and usually execute more slowly
Answer: A,B,C,D
NEW QUESTION # 34
What is the formula for measuring skewness in a dataset?
- A. MODE - MEDIAN
- B. (MEAN - MODE)/ STANDARD DEVIATION
- C. (3(MEAN - MEDIAN))/ STANDARD DEVIATION
- D. MEAN - MEDIAN
Answer: C
Explanation:
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.
NEW QUESTION # 35
Select the Correct Statements regarding Normalization?
- A. Scikit-Learn provides a transformer RecommendedScaler for Normalization.
- B. Normalization technique uses mean and standard deviation for scaling of model.
- C. Normalization got affected by outliers.
- D. Normalization technique uses minimum and max values for scaling of model.
Answer: C,D
Explanation:
Explanation
Normalization is a scaling technique in Machine Learning applied during data preparation to change the values of numeric columns in the dataset to use a common scale.It is not necessary for all datasets in a model. It is required only when features of machine learning models have different ranges.
Scikit-Learn provides a transformer called MinMaxScaler for Normalization.
This technique uses minimum and max values for scaling of model.Itis useful when feature distribution is unknown.It got affected by outliers.
NEW QUESTION # 36
Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share us-ing Which of the following options?
- A. Grant privileges on objects to a share via a database role.
- B. Grant privileges on objects to a share via a third-party role.
- C. Grant privileges on objects directly to a share.
- D. Grant privileges on objects to a share via Account role.
Answer: A,C
Explanation:
ExplanationWhat 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.
NEW QUESTION # 37
Secure Data Sharing do not let you share which of the following selected objects in a database in your account with other Snowflake accounts?
- A. Secure UDFs
- B. Tables
- C. External tables
- D. Sequences
Answer: D
Explanation:
Explanation
Secure Data Sharing lets you share selected objects in a database in your account with other Snow-flake accounts. You can share the following Snowflake database objects:
Tables
External tables
Secure views
Secure materialized views
Secure UDFs
Snowflake enables the sharing of databases through shares, which are created by data providers and
"imported" by data consumers.
NEW QUESTION # 38
Which of the following Functions do Support Windowing?
- A. HASH_AGG
- B. EXTRACT
- C. LISTAGG
- D. ENCRYPT
Answer: C
Explanation:
Explanation
What is a Window?
A window is a group of related rows. For example, a window might be defined based on timestamps, with all rows in the same month grouped in the same window. Or a window might be defined based on location, with all rows from a particular city grouped in the same window.
A window can consist of zero, one, or multiple rows. For simplicity, Snowflake documentation usually says that a window contains multiple rows.
What is a Window Function?
A window function is any function that operates over a window of rows.
A window function is generally passed two parameters:
A row. More precisely, a window function is passed 0 or more expressions. In almost all cases, at least one of those expressions references a column in that row. (Most window functions require at least one column or expression, but a few window functions, such as some rank-related functions, do not required an explicit column or expression.) A window of related rows that includes that row. The window can be the entire table, or a subset of the rows in the table.
For non-window functions, all arguments are usually passed explicitly to the function, for example:
MY_FUNCTION(argument1, argument2, ...)
Window functions behave differently; although the current row is passed as an argument the normal way, the window is passed through a separate clause, called an OVER clause. The syntax of the OVER clause is documented later.
LISTAGG
Returns the concatenated input values, separated by the delimiter string.
Window function
1.LISTAGG( [ DISTINCT ] <expr1> [, <delimiter> ] )
2.[ WITHIN GROUP ( <orderby_clause> ) ]
3.OVER ( [ PARTITION BY <expr2> ] )
HASH_AGG
Returns an aggregate signed 64-bit hash value over the (unordered) set of input rows. HASH_AGG never returns NULL, even if no input is provided. Empty input "hashes" to 0.
Window function
HASH_AGG( [ DISTINCT ] <expr> [ , <expr2> ... ] ) OVER ( [ PARTITION BY <expr3> ] ) HASH_AGG(*) OVER ( [ PARTITION BY <expr3> ] )
NEW QUESTION # 39
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