The SnowPro Advanced: Data Engineer Mock test validates advanced knowledge and skills used to apply comprehensive data engineering principles using Snowflake.

Note: This is Mock Test & Do not assume it as Exam Dump.

This Practice Mock Test will test the ability of Candidate to:

● Source data from Data Lakes, APIs, and on-premises

● Transform, replicate, and share data across cloud platforms

● Design end-to-end near real-time streams

● Design scalable compute solutions for DE workloads

● Evaluate performance metrics

Domain Estimated                  Percentage Range of Exam Questions

1.0 Data Movement                           28%

2.0 Performance Optimization        22%

3.0 Storage and Data Protection    10%

4.0 Security                                          10%

5.0 Data Transformation                    30%

1.0 Domain: Data Movement

1.1 Given a data set, load data into Snowflake.

● Outline considerations for data loading

● Define data loading features and potential impact

1.2 Ingest data of various formats through the mechanics of Snowflake.

● Required data formats

● Outline Stages

1.3 Troubleshoot data ingestion.

1.4 Design, build and troubleshoot continuous data pipelines.

● Design a data pipeline that forces uniqueness but is not unique.

● Stages

● Tasks

● Streams

● Snowpipe

● Auto ingest as compared to Rest API

1.5 Analyze and differentiate types of data pipelines.

1.6 Install, configure, and use connectors to connect to Snowflake.

1.7 Design and build data sharing solutions.

● Implement a data share

● Create a secure view

● Implement row level filtering

1.8 Outline when to use an External Table and define how they work.

● Partitioning external tables

● Materialized views

● Partitioned data unloading

2.0 Domain: Performance Optimization

2.1 Troubleshoot underperforming queries.

● Identify underperforming queries

● Outline telemetry around the operation

● Increase efficiency

● Identify the root cause

2.2 Given a scenario, configure a solution for the best performance.

● Scale out vs. scale in

● Cluster vs. increase warehouse size

● Query complexity

● Micro partitions and the impact of clustering

● Materialized views

● Search optimization

2.3 Outline and use caching features.

2.4 Monitor continuous data pipelines.

• Snowpipe

• Stages

• Tasks

• Streams

  
  

3.0 Domain: Storage & Data Protection

3.1 Implement data recovery features in Snowflake.

● Time Travel

● Fail-safe

3.2 Outline the impact of Streams on Time Travel.

3.3 Use System Functions to analyze Micro-partitions.

● Clustering depth

● Cluster keys

3.4 Use Time Travel and Cloning to create new development environments.

● Backup databases

● Test changes before deployment

● Rollback

4.0 Domain: Security

4.1 Outline Snowflake security principles.

● Authentication methods (Single Sign On, Key Authentication,

Username/Password, MFA)

● Role Based Access Control (RBAC)

● Column level security and how data masking works with RBAC to secure sensitive data

4.2 Outline the System Defined Roles and when they should be applied.

● The purpose of each of the System Defined Roles including best practices

usage in each case

● The primary differences between SECURITYADMIN and USERADMIN roles

● The difference between the purpose and usage of the

USERADMIN/SECURITYADMIN roles and SYSADMIN

4.3 Manage data governance.

● Explain the options available to support column level security including

Dynamic Data Masking and external tokenization

● Explain the options available to support row level security using Snowflake

row access policies

● Use DDL required to manage Dynamic Data Masking and row access policies

● Use methods and best practices for creating and applying masking policies on

data

● Use methods and best practices for object tagging

5.0 Domain: Data Transformation

5.1 Define User-Defined Functions (UDFs) and outline how to use them.

● Secure UDFs

● SQL UDFs

● JavaScript UDFs

● Returning table value as compared to scalar value

5.2 Define and create external functions.

● Secure external functions

5.3 Design, build, and leverage stored procedures.

● Transaction management

5.4 Handle and transform semi-structured data.

● Traverse and transform semi-structured data to structured data

● Transform structured to semi-structured data

5.5 Use Snowpark for data transformation.

● Query and filter data using the Snowpark library

● Perform data transformations using Snowpark (ie., aggregations)

● Join Snowpark dataframes