In order to obtain your SnowPro Advanced: Data Scientist Certification, it is essential to showcase your comprehensive knowledge and proficiency in utilizing fundamental concepts, tools, and methodologies of data science in conjunction with Snowflake. We are pleased to inform you that Passcert has recently released a set of newly cracked SnowPro Advanced: Data Scientist DSA-C02 Exam Dumps which are designed to assist you in thoroughly preparing for your upcoming test and ensuring a successful pass in your DSA-C02 exam. These SnowPro Advanced: Data Scientist DSA-C02 Exam Dumps have been meticulously crafted to cover all the necessary topics and provide you with the necessary resources to enhance your understanding and skills. With these exam dumps at your disposal, you can confidently approach your certification journey and achieve your desired outcome.

The SnowPro Advanced: Data Scientist Certification exam will test advanced knowledge and skills used to apply comprehensive data science principles, tools, and methodologies using Snowflake. This certification will test the ability to:

● Outline data science concepts

● Implement Snowflake data science best practices

● Prepare data and feature engineering in Snowflake

● Train and use machine learning models

● Use data visualization to present a business case (e.g., model explainability)

● Implement model lifecycle management

 

We recommend individuals have at least 2 + years of hands-on Snowflake Practitioner experience in a Data Scientist role prior to attempting this exam. The exam will assess skills through scenario-based questions and real-world examples.

 

Exam Version: DSA-C02

Total Number of Questions: 65

Question Types: Multiple Select, Multiple Choice

Time Limit: 115 minutes

Language: English

Registration fee: $375 USD

Passing Score: 750 + Scaled Scoring from 0 - 1000

Prerequisites: SnowPro Core Certified

Delivery Options: 1 Online Proctoring  2 Onsite Testing Centers

 

1.0 Data Science Concepts 15%

2.0 Data Pipelining 19%

3.0 Data Preparation and Feature Engineering 30%

4.0 Model Development 20%

5.0 Model Deployment 16%

 

1.1 Define machine learning concepts for data science workloads.

1.2 Outline machine learning problem types.

1.3 Summarize the machine learning lifecycle.

1.4 Define statistical concepts for data science.

 

2.1 Enrich data by consuming data sharing sources.

2.2 Build a data science pipeline.

 

3.1 Prepare and clean data in Snowflake.

3.2 Perform exploratory data analysis in Snowflake.

3.3 Perform feature engineering on Snowflake data.

3.4 Visualize and interpret the data to present a business case.

 

4.1 Connect data science tools directly to data in Snowflake.

4.2 Train a data science model.

4.3 Validate a data science model.

4.4 Interpret a model.

 

5.1 Move a data science model into production.

5.2 Determine the effectiveness of a model and retrain if necessary.

5.3 Outline model lifecycle and validation tools

 

1. Which ones are the correct rules while using a data science model created via External function in Snowflake?

A.External functions return a value. The returned value can be a compound value, such as a VARIANT that contains JSON.

B.External functions can be overloaded.

C.An external function can appear in any clause of a SQL statement in which other types of UDF can appear.

D.External functions can accept Model parameters.

Answer: A, B, C, D

 

2. 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

Answer: C

 

3. Which ones are the type of visualization used for Data exploration in Data Science?

A.Heat Maps

B.Newton AI

C.Feature Distribution by Class

D.2D-Density Plots

E.Sand Visualization

Answer: A, D, E

 

4. 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

 

5. Which of the following Snowflake parameter can be used to Automatically Suspend Tasks which are running Data science pipelines after specified Failed Runs?

A.SUSPEND_TASK

B.SUSPEND_TASK_AUTO_NUM_FAILURES

C.SUSPEND_TASK_AFTER_NUM_FAILURES

D.There is none as such available.

Answer: C

 

6. 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.

Answer: B