Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

Snowflake DSA-C02 Exam

Exam Name: SnowPro Advanced: Data Scientist Certification Exam
Exam Code: DSA-C02
Related Certification(s):
  • Snowflake SnowPro Certification Certifications
  • Snowflake SnowPro Advanced Certification Certifications
Certification Provider: Snowflake
Number of DSA-C02 practice questions in our database: 65 (updated: Jun. 04, 2024)
Disscuss Snowflake DSA-C02 Topics, Questions or Ask Anything Related

Currently there are no comments in this discussion, be the first to comment!

Free Snowflake DSA-C02 Exam Actual Questions

Note: Premium Questions for DSA-C02 were last updated On Jun. 04, 2024 (see below)

Question #1

Which tools helps data scientist to manage ML lifecycle & Model versioning?

Reveal Solution Hide Solution
Correct Answer: A, B

Model versioning in a way involves tracking the changes made to an 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.


Question #2

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?

Reveal Solution Hide Solution
Correct Answer: C

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 #3

Which tools helps data scientist to manage ML lifecycle & Model versioning?

Reveal Solution Hide Solution
Correct Answer: A, B

Model versioning in a way involves tracking the changes made to an 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.


Question #4

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?

Reveal Solution Hide Solution
Correct Answer: C

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 #5

Mark the incorrect statement regarding usage of Snowflake Stream & Tasks?

Reveal Solution Hide Solution
Correct Answer: D

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).



Unlock Premium DSA-C02 Exam Questions with Advanced Practice Test Features:
  • Select Question Types you want
  • Set your Desired Pass Percentage
  • Allocate Time (Hours : Minutes)
  • Create Multiple Practice tests with Limited Questions
  • Customer Support
Get Full Access Now

Save Cancel