Description

SnowPro® Advanced: Data Analyst Overview

The SnowPro® Advanced: Data Analyst Certification Exam will test advanced knowledge and skills used to apply comprehensive data analysis principles using Snowflake and its components. This certification will test the ability to:

1. Prepare and load data
2. Perform simple data transformations for data analysis
3. Build and troubleshoot advanced SQL queries in Snowflake
4. Use Snowflake built-in functions and create User-Defined Functions (UDFs)
5. Perform descriptive and diagnostic data analyses
6. Perform data forecasting
7. Prepare and present data to meet business requirements

SnowPro® Advanced: Data Analyst Candidate

1+ year of Snowflake data cloud analytics experience, including practical, hands-on use of the Snowflake Data Cloud. The candidate must hold the SnowPro Core certification in good standing and should have fluency with advanced SQL. Knowledge of an additional computer language is recommended but not required.

SnowPro® Advanced: Data Analyst Exam Details

Target Audience:

1. Snowflake Data Analysts
2. ELT Developers
3. BI Specialists

Exam Format:

1. Exam Version: DAA-C01
2. Total Number of Questions: 65
3. Question Types: Multiple Select, Multiple Choice
4. Time Limits: 115 minutes
5. Language: English
6. Registration fee: USD 375
7. Passing Score: 750 + Scaled Scoring from 0 – 1000

Unscored Content:

Exams may include unscored items to gather statistical information for future use. These items are not identified on the form and do not impact your score, and additional time is factored into account for this content.

Prerequisites:

SnowPro Core Certified

Delivery Options:

1. Online Proctoring
2. Onsite Testing Centers

Exam Domain Breakdown:

This exam guide includes test domains, weightings, and objectives. It is not a comprehensive listing of all the content that will be presented in this examination. The table below lists the main content domains and their weightings.

Exam Topics:

Outlined below are the Domains & Objectives measured on the exam. To view subtopics, download the exam study guide.

Data Ingestion and Data Preparation

1. Use a collection system to retrieve data.
2. Perform data discovery to identify what is needed from the available datasets.
3. Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.
4. Outline and use best practice considerations relating to data integrity structures.
5. Implement data processing solutions.
6. Given a scenario, prepare data and load it into Snowflake.
7. Given a scenario, use Snowflake functions.

Data Transformation and Data Modeling

1. Prepare different data types into a consumable format.
2. Given a dataset, clean the data.
3. Given a dataset or scenario, work with and query the data.
4. Use data modeling to manipulate the data to meet BI requirements.
5. Optimize query performance.

Data Analysis

1. Use SQL extensibility features.
2. Perform a descriptive analysis.
3. Perform a diagnostic analysis.
4. Perform forecasting.

Data Presentation and Data Visualization

1. Given a use case, create reports and dashboards to meet business requirements.
2. Given a use case, maintain reports and dashboards to meet business requirements.
3. Given a use case, incorporate visualizations for dashboards and reports.

TABLE OF CONTENTS

Outline key features of the Snowflake Data Cloud.

1. Elastic Storage
2. Elastic Compute
3. Snowflake’s three distinct layers
4. Cloud partner categories
5. Overview of Snowflake editions

Outline key Snowflake tools and user interfaces.

1. Snowsight
2. SnowSQL
3. Snowflake connectors
4. Snowflake drivers
5. Snowpark
6. SnowCD

Outline Snowflake’s catalog and objects.

Snowflake Data Cloud Features and Architecture:

1. Databases
2. Stages
3. Schema types
4. Table types
5. View types
6. Data types
7. User-Defined Functions (UDFs)
8. User Defined Table Functions (UDTFs)
9. Stored Procedures
10. Streams
11. Tasks
12. Pipes
13. Shares
14. Sequences

Outline Snowflake storage concepts.

1. Micro-partitions
2. Data clustering
3. Data Storage Monitoring

Outline security principles.

1. Network security and policies
2. Multi-Factor Authentication (MFA)
3. Federated authentication
4. Key pair authentication
5. Single Sign-On (SSO)

Define the entities and roles that are used in Snowflake.

-Overview of access control
– Access control frameworks
– Access control privileges

Account Access and Security:

1. Outline how privileges can be granted and revoked
2. Explain role hierarchy and privilege inheritance

Outline data governance capabilities in Snowflake.

1. Accounts
2. Organizations
3. Secure views
4. Secure functions
5. Information schemas
6. Access history
   – Tracking read/write operations
7. Overview of row/column-level security
8. Object tags

Explain the use of the Query Profile.

1. Explain plans
2. Data spilling
3. Use of the data cache
4. Micro-partition pruning
5. Query history

Explain virtual warehouse configurations.

1. Types of warehouses
2. Multi-clustering warehouses

– Scaling policies
– Scaling modes

1. Warehouse sizing
2. Warehouse settings and access

Outline virtual warehouse performance tools.

Performance Concepts:

1. Monitoring warehouse loads
2. Scaling up compared to scaling out
3. Resource monitors
4. Query acceleration service

Optimize query performance.

1. Describe the use of materialized views
2. Use of specific SELECT commands
3. Clustering
4. Search optimization service
5. Persisted query results
6. Understanding the impact of different types of caching
   – Metadata cache
   – Result cache
   – Warehouse cache

Define concepts and best practices that should be considered when loading data.

1. Stages and stage types
2. File size and formats
3. Folder structures
4. Ad hoc/bulk loading
5. Snowpipe

Outline different commands used to load data and when they should be used.

1. CREATE STAGE
2. CREATE FILE FORMAT
3. CREATE PIPE
4. CREATE EXTERNAL TABLE
5. COPY INTO
6. INSERT/INSERT OVERWRITE

Data Loading and Unloading:

1. PUT
2. VALIDATE

Define concepts and best practices that should be considered when unloading data.

1. File size and formats
   – Overview of compression methods
2. Empty strings and NULL values
3. Unloading to a single file
4. Unloading relational tables

Outline the commands used to unload data and when they should be used.

1. GET
2. LIST
3. COPY INTO
4. CREATE STAGE
5. CREATE FILE FORMAT

Explain how to work with standard data.

1. Estimation functions
2. Sampling
   – SAMPLE command
   – /TABLESAMPLE command
   – Sampling methods
   1. Fraction-based
   2. Fixed-size
3. Supported function types
   – System functions
   – Table functions
   – External functions
   – User-Defined Functions (UDFs)
4. Stored procedures
5. Streams
6. Tasks

Data Transformations:

Explain how to work with semi-structured data.

1. Supported data formats, data types, and sizes
2. VARIANT column
3. Flattening the nested structure
   – FLATTEN command
   – LATERAL FLATTEN command
4. Semi-structured data functions
   – ARRAY/OBJECT creation and manipulation
   – Extracting values
   – Type predicates

Explain how to work with unstructured data.

1. Define and use directory tables
2. SQL file functions
   – Types of URLs available to access files
3. Outline the purpose of User-Defined Functions (UDFs) for data analysis

Outline Continuous Data Protection with Snowflake.

1. Time Travel
2. Fail-safe
3. Data Encryption
4. Cloning
5. Replication

Data Protection and Data Sharing:

Outline Snowflake data sharing capabilities.

1. Account types
2. Snowflake Marketplace
3. Data exchange
4. Access control options
   – DDL commands to create and manage shares
   – Privileges required for working with shares
5. Secure Data Sharing (for example, Direct Share, Listing)

For more information on SnowPro® Advanced Data Analyst; please visit here.

Contact Locus IT support team for further training details.