Overview

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 metricsDomain 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 Movement1.1 Given a data set, load data into Snowflake.● Outline considerations for data loading● Define data loading features and potential impact1.2 Ingest data of various formats through the mechanics of Snowflake.● Required data formats● Outline Stages1.3 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 API1.5 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 filtering1.8 Outline when to use an External Table and define how they work.● Partitioning external tables● Materialized views● Partitioned data unloading2.0 Domain: Performance Optimization2.1 Troubleshoot underperforming queries.● Identify underperforming queries● Outline telemetry around the operation● Increase efficiency● Identify the root cause2.2 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 optimization2.3 Outline and use caching features.2.4 Monitor continuous data pipelines.SnowpipeStagesTasksStreams3.0 Domain: Storage & Data Protection3.1 Implement data recovery features in Snowflake.● Time Travel● Fail-safe3.2 Outline the impact of Streams on Time Travel.3.3 Use System Functions to analyze Micro-partitions.● Clustering depth● Cluster keys3.4 Use Time Travel and Cloning to create new development environments.● Backup databases● Test changes before deployment● Rollback4.0 Domain: Security4.1 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 data4.2 Outline the System Defined Roles and when they should be applied.● The purpose of each of the System Defined Roles including best practicesusage in each case● The primary differences between SECURITYADMIN and USERADMIN roles● The difference between the purpose and usage of theUSERADMIN/SECURITYADMIN roles and SYSADMIN4.3 Manage data governance.● Explain the options available to support column level security includingDynamic Data Masking and external tokenization● Explain the options available to support row level security using Snowflakerow 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 ondata● Use methods and best practices for object tagging5.0 Domain: Data Transformation5.1 Define User-Defined Functions (UDFs) and outline how to use them.● Secure UDFs● SQL UDFs● JavaScript UDFs● Returning table value as compared to scalar value5.2 Define and create external functions.● Secure external functions5.3 Design, build, and leverage stored procedures.● Transaction management5.4 Handle and transform semi-structured data.● Traverse and transform semi-structured data to structured data● Transform structured to semi-structured data5.5 Use Snowpark for data transformation.● Query and filter data using the Snowpark library● Perform data transformations using Snowpark (ie., aggregations)● Join Snowpark dataframes

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