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NEW QUESTION # 24
Which tools helps data scientist to manage ML lifecycle & Model versioning?
- A. CRUX
- B. Pachyderm
- C. MLFlow
- D. Albert
Answer: B,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 # 25
Which metric is not used for evaluating classification models?
- A. Accuracy
- B. Precision
- C. Recall
- D. Mean absolute error
Answer: D
Explanation:
Explanation
The four commonly used metrics for evaluating classifier performance are:
1. Accuracy: The proportion of correct predictions out of the total predictions.
2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.
NEW QUESTION # 26
Data Scientist can query, process, and transform data in a which of the following ways using Snowpark Python. [Select 2]
- A. Query and process data with a DataFrame object.
- B. Write a user-defined tabular function (UDTF) that processes data and returns data in a set of rows with one or more columns.
- C. Transform Data using DataIKY tool with SnowPark API.
- D. SnowPark currently do not support writing UDTF.
Answer: A,D
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 # 27
Mark the incorrect statement regarding Python UDF?
- A. A scalar function (UDF) returns a tabular value for each input row
- B. A UDF also gives you a way to encapsulate functionality so that you can call it repeatedly from multiple places in code
- C. Python UDFs can contain both new code and calls to existing packages
- D. For each row passed to a UDF, the UDF returns either a scalar (i.e. single) value or, if defined as a table function, a set of rows.
Answer: A
Explanation:
Explanation
A scalar function (UDF) returns one output row for each input row. The returned row consists of a single column/value
NEW QUESTION # 28
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. Tags
- B. Dynamic tables
- C. Task
- D. Delta
- E. Stream
- F. OFFSET
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 # 29
Mark the Incorrect understanding of Data Scientist about Streams?
- A. Streams do not support repeatable read isolation.
- B. Streams itself does not contain any table data.
- C. Streams can track changes in materialized views.
- D. Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views.
Answer: A,C
Explanation:
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.
NEW QUESTION # 30
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 # 31
What is the formula for measuring skewness in a dataset?
- A. (MEAN - MODE)/ STANDARD DEVIATION
- B. MODE - MEDIAN
- 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 # 32
A Data Scientist as data providers require to allow consumers to access all databases and database objects in a share by granting a single privilege on shared databases. Which one is incorrect SnowSQL command used by her while doing this task?
Assuming:
A database named product_db exists with a schema named product_agg and a table named Item_agg.
The database, schema, and table will be shared with two accounts named xy12345 and yz23456.
1.USE ROLE accountadmin;
2.CREATE DIRECT SHARE product_s;
3.GRANT USAGE ON DATABASE product_db TO SHARE product_s;
4.GRANT USAGE ON SCHEMA product_db. product_agg TO SHARE product_s;
5.GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
6.SHOW GRANTS TO SHARE product_s;
7.ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
8.SHOW GRANTS OF SHARE product_s;
- A. ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
- B. CREATE DIRECT SHARE product_s;
- C. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
- D. GRANT USAGE ON DATABASE product_db TO SHARE product_s;
Answer: C
Explanation:
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
NEW QUESTION # 33
Which one is incorrect understanding about Providers of Direct share?
- A. If you want to provide a share to many accounts, you can do the same via Direct Share.
- B. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts to consume.
- C. As a data provider, you share a database with one or more Snowflake accounts.
- D. You can create as many shares as you want, and add as many accounts to a share as you want.
Answer: A
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 # 34
Which of the following is a common evaluation metric for binary classification?
- A. Accuracy
- B. Mean squared error (MSE)
- C. Area under the ROC curve (AUC)
- D. F1 score
Answer: C
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 # 35
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. Streamsets
- B. StreamBI
- C. Streamlit
- D. Rapter
Answer: C
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 # 36
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?
- A. Data Virtualization
- B. Data visualization
- C. Data Interpretation
- D. Data Mining
Answer: D
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 # 37
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 can be overloaded.
- 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 # 38
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 # 39
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 # 40
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