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NEW QUESTION # 27
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. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
- B. Create a new version of the dataset using only the new data and retrain the model.
- C. 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.
- D. Retrained your training dataset after correcting data outliers & no need to introduce new data.
Answer: C
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 # 28
Data Scientist can query, process, and transform data in a which of the following ways using Snowpark Python. [Select 2]
- A. SnowPark currently do not support writing UDTF.
- B. Query and process data with a DataFrame object.
- C. Transform Data using DataIKY tool with SnowPark API.
- D. Write a user-defined tabular function (UDTF) that processes data and returns data in a set of rows with one or more columns.
Answer: A,B
Explanation:
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.
NEW QUESTION # 29
Which method is used for detecting data outliers in Machine learning?
- A. CMIYC
- B. Scaler
- C. BOXI
- D. Z-Score
Answer: D
Explanation:
Explanation
What are outliers?
Outliers are the values that look different from the other values in the data. Below is a plot high-lighting the outliers in 'red' and outliers can be seen in both the extremes of data.
Reasons for outliers in data
Errors during data entry or a faulty measuring device (a faulty sensor may result in extreme readings).
Natural occurrence (salaries of junior level employees vs C-level employees) Problems caused by outliers Outliers in the data may causes problems during model fitting (esp. linear models).
Outliers may inflate the error metrics which give higher weights to large errors (example, mean squared error, RMSE).
Z-score method is of the method for detecting outliers. This methodis generally used when a variable' distribution looks close to Gaussian. Z-score is the number of standard deviations a value of a variable is away from the variable' mean.
Z-Score = (X-mean) / Standard deviation
IQR method , Box plots are some more example of methods used to detect data outliers in Data science.
NEW QUESTION # 30
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 TASK
- B. CALL TASK
- C. RUN ROOT TASK
- D. EXECUTE TASK
Answer: D
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 # 31
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.
Answer: C
Explanation:
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.
NEW QUESTION # 32
All aggregate functions except _____ ignore null values in their input collection
- A. Count(attribute)
- B. Avg
- C. Sum
- D. Count(*)
Answer: D
Explanation:
Explanation
Count(*)
* is used to select all values including null.
NEW QUESTION # 33
Which of the following method is used for multiclass classification?
- A. one vs rest
- B. all vs one
- C. loocv
- D. one vs another
Answer: A
Explanation:
Explanation
Binary vs. Multi-Class Classification
Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.
As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data.
The technique enables developers to categorize the test data into multiple binary class labels.
That said, while binary classification requires only one classifier model, the one used in the multi-class approach depends on the classification technique. Below are the two models of the multi-class classification algorithm.
One-Vs-Rest Classification Model for Multi-Class Classification
Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.
For instance, with a multi-class classification problem with red, green, and blue datasets, binary classification can be categorized as follows:
Problem one: red vs. green/blue
Problem two: blue vs. green/red
Problem three: green vs. blue/red
The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.
The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used forclassification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.
NEW QUESTION # 34
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the aggregate method shown in below code do?
g = df.groupby(df.index.str.len())
g.aggregate({'A':len, 'B':np.sum})
- A. Computes length of column A
- B. Computes length of column A and Sum of Column B values of each group
- C. Computes length of column A and Sum of Column B values
- D. Computes Sum of column A values
Answer: B
Explanation:
Explanation
Computes length of column A and Sum of Column B values of each group
NEW QUESTION # 35
Which one is the incorrect option to share data in Snowflake?
- A. a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
- 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 Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
- D. a Direct Share, in which you directly share specific database objects (a share) to anoth-er account in your region.
Answer: B
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 # 36
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the expression g = df.groupby(df.index.str.len()) do?
- A. Data frames cannot be grouped by index values. Hence it results in Error.
- B. Groups df based on length of each index value
- C. Groups df based on index values
- D. Groups df based on index strings
Answer: A
Explanation:
Explanation
Data frames cannot be grouped by index values. Hence it results in Error.
NEW QUESTION # 37
Which 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 of Data Science Pipelines?
- A. OFFSET
- B. Delta
- C. Task
- D. Tags
- E. Stream
- F. Dynamic tables
Answer: E
Explanation:
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
NEW QUESTION # 38
Which tools helps data scientist to manage ML lifecycle & Model versioning?
- A. Pachyderm
- B. Albert
- C. MLFlow
- D. CRUX
Answer: A,C
Explanation:
Explanation
Model versioning in a way involves tracking the changes made toan ML model that has been previously built.
Put differently, it is the process of making changes to the configurations of an ML Model. From another perspective, we can see model versioning as a feature that helps Machine Learning Engineers, Data Scientists, and related personnel create and keep multiple versions of the same model.
Think of it as a way of taking notes of the changes you make to the model through tweaking hyperparameters, retraining the model with more data, and so on.
In model versioning, a number of things need to be versioned, to help us keep track of important changes. I'll list and explain them below:
Implementation code: From the early days of model building to optimization stages, code or in this case source code of the model plays an important role. This code experiences significant changes during optimization stages which can easily be lost if not tracked properly. Because of this, code is one of the things that are taken into consideration during the model versioning process.
Data: In some cases, training data does improve significantly from its initial state during model op-timization phases. This can be as a result of engineering new features from existing ones to train our model on. Also there is metadata (data about your training data and model) to consider versioning. Metadata can change different times over without the training data actually changing. We need to be able to track these changes through versioning Model: The model is a product of the two previous entities and as stated in their explanations, an ML model changes at different points of the optimization phases through hyperparameter setting, model artifacts and learning coefficients. Versioning helps take record of the different versions of a Machine Learning model.
MLFlow & Pachyderm are the tools used to manage ML lifecycle & Model versioning.
NEW QUESTION # 39
There are a couple of different types of classification tasks in machine learning, Choose the Correct Classification which best categorized the below Application Tasks in Machine learning?
To detect whether email is spam or not
To determine whether or not a patient has a certain disease in medicine.
To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).
- A. Multi-Class Classification
- B. Multi-Label Classification
- C. Logistic Regression
- D. Binary Classification
Answer: D
Explanation:
Explanation
The Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms.
What is the Classification Algorithm?
The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. Such as, Yes or No, 0 or
1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories.
Unlike regression, the output variable of Classification is a category, not a value, such as "Green or Blue",
"fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output.
In classification algorithm, a discrete output function(y) is mapped to input variable(x).
y=f(x), where y = categorical output
The best example of an ML classification algorithm is Email Spam Detector.
The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.
The algorithm which implements the classification on a dataset is known as a classifier. There are two types of Classifications:
Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier.
Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc.
Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier.
Example: Classifications of types of crops, Classification of types of music.
Binary classification in deep learning refers to the type of classification where we have two class labels - one normal and one abnormal. Some examples of binary classification use:
To detect whether email is spam or not
To determine whether or not a patient has a certain disease in medicine.
To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).
For example, the normal class label would be that a patient has the disease, and the abnormal class label would be that they do not, or vice-versa.
As is with every other type of classification, it is only as good as the binary classification dataset that it has - or, in other words, the more training and data it has, the better it is.
NEW QUESTION # 40
In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
- A. no change
- B. by its slope
- C. by intercept
- D. by 1
Answer: B
Explanation:
Explanation
What is linear regression?
Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatoryvariable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
For linear regression Y=a+bx+error.
If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.
For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.
NEW QUESTION # 41
Which one of the following is not the key component while designing External functions within Snowflake?
- A. API Integration
- B. UDF Service
- C. Proxy Service
- D. Remote Service
Answer: B
Explanation:
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.
NEW QUESTION # 42
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. Tables
- B. External tables
- C. Secure UDFs
- 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 # 43
Which of the Following is not type of Windows function in Snowflake?
- A. Rank-related functions.
- B. Aggregation window functions.
- C. Window frame functions.
- D. Association functions.
Answer: B,D
Explanation:
Explanation
Window Functions
A window function operates on a group ("window") of related rows.
Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function.
Some window functions are order-sensitive. There are two main types of order-sensitive window functions:
Rank-related functions.
Window frame functions.
Rank-related functions list information based on the "rank" of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.
Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.
NEW QUESTION # 44
Which ones are the correct rules while using a data science model created via External function in Snowflake?
- A. External functions can be overloaded.
- B. An external function can appear in any clause of a SQL statement in which other types of UDF can appear.
- C. External functions can accept Model parameters.
- D. External functions return a value. The returned value can be a compound value, such as a VARIANT that contains JSON.
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 # 45
To return the contents of a DataFrame as a Pandas DataFrame, Which of the following method can be used in SnowPark API?
- A. TO_PANDAS
- B. CONVERT_TO_PANDAS
- C. SNOWPARK_TO_PANDAS
- D. REPLACE_TO_PANDAS
Answer: A
Explanation:
Explanation
To return the contents of a DataFrame as a Pandas DataFrame, use the to_pandas method.
For example:
1.>>> python_df = session.create_dataframe(["a", "b", "c"])
2.>>> pandas_df = python_df.to_pandas()
NEW QUESTION # 46
Which one is not Types of Feature Scaling?
- A. Economy Scaling
- B. Min-Max Scaling
- C. Standard Scaling
- D. Robust Scaling
Answer: B
Explanation:
ExplanationFeature Scaling
Feature Scaling is the process of transforming the features so that they have a similar scale. This is important in machine learning because the scale of the features can affect the performance of the model.
Types of Feature Scaling:
Min-Max Scaling: Rescaling the features to a specific range, such as between 0 and 1, by subtracting the minimum value and dividing by the range.
Standard Scaling: Rescaling the features to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.
Robust Scaling: Rescaling the features to be robust to outliers by dividing them by the interquartile range.
Benefits of Feature Scaling:
Improves Model Performance: By transforming the features to have a similar scale, the model can learn from all features equally and avoid being dominated by a few large features.
Increases Model Robustness: By transforming the features to be robust to outliers, the model can become more robust to anomalies.
Improves Computational Efficiency: Many machine learning algorithms, such as k-nearest neighbors, are sensitive to the scale of the features and perform better with scaled features.
Improves Model Interpretability: By transforming the features to have a similar scale, it can be easier to understand the model's predictions.
NEW QUESTION # 47
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
- A. Receive frictionless access to raw data products from vendors.
- B. Discover and test third-party data sources.
- C. Combine new datasets with your existing data in Snowflake to derive new business in-sights.
- D. Use the business intelligence (BI)/ML/Deep learning tools of her choice.
Answer: A,B,C,D
Explanation:
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.
NEW QUESTION # 48
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