Snowflake SnowPro Advanced - Data Analyst Certification Exam Syllabus

DAA-C01 Dumps Questions, DAA-C01 PDF, SnowPro Advanced - Data Analyst Exam Questions PDF, Snowflake DAA-C01 Dumps Free, SnowPro Advanced - Data Analyst Official Cert Guide PDFThe Snowflake DAA-C01 exam preparation guide is designed to provide candidates with necessary information about the SnowPro Advanced - Data Analyst exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Snowflake Certified SnowPro Advanced - Data Analyst exam.

It is recommended for all the candidates to refer the DAA-C01 objectives and sample questions provided in this preparation guide. The Snowflake SnowPro Advanced - Data Analyst certification is mainly targeted to the candidates who want to build their career in Advance domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Snowflake SnowPro Advanced - Data Analyst exam.

Snowflake DAA-C01 Exam Summary:

Exam Name
Snowflake SnowPro Advanced - Data Analyst
Exam Code DAA-C01
Exam Price $375 USD
Duration 115 minutes
Number of Questions 65
Passing Score 750 + Scaled Scoring from 0 - 1000%
Recommended Training / Books Snowflake Data Analyst Training
SnowPro Advanced: Data Analyst Exam Study Guide
Schedule Exam PEARSON VUE
Sample Questions Snowflake DAA-C01 Sample Questions
Recommended Practice Snowflake Certified SnowPro Advanced - Data Analyst Practice Test

Snowflake SnowPro Advanced - Data Analyst Syllabus:

Section Objectives Weight
Data Ingestion and Data Preparation

Use a collection system to retrieve data.
- Assess how often data needs to be collected
- Identify the volume of data to be collected
- Identify data sources
- Retrieve data from a source

Perform data discovery to identify what is needed from the available datasets.
- Query tables in Snowflake
- Evaluate which transformations are required

Enrich data by identifying and accessing relevant data from the Snowflake Marketplace.
- Find external data sets that correlate with available data
- Use data shares to join data with existing data sets
- Create tables and views

Outline and use best practice considerations relating to data integrity structures.
- Primary keys for tables
- Perform table joins between parent/child tables
- Constraints

Implement data processing solutions.
- Aggregate and enrich data
- Automate and implement data processing
- Respond to processing failures
- Use logging and monitoring solutions

Given a scenario, prepare data and load into Snowflake.
- Load files using Snowsight
- Load data from external/internal stages into a Snowflake table
- Load different types of data
- Perform general DML (insert, update, delete)
- Identify and resolve data import errors

Given a scenario, use Snowflake functions.
- Scalar functions
- Aggregate functions
- Window functions
- Table functions
- System functions
- Geospatial functions

Data Transformation and Data Modeling

Prepare different data types into a consumable format.
- JSON (query and parse)
- Parquet

Given a dataset, clean the data.
- Identify and analyze data anomalies
- Handle erroneous data
- Validate data types
- Use clones as required by specific use-cases

Given a dataset or scenario, work with and query the data.
- Aggregate and validate the data.
- Apply analytic functions
- Perform pre-math calculations (examples, randomization, ranking, grouping, min/max)
- Perform classifications
- Perform casting - change data types to ensure data can be presented consistently
- Enrich the data
- Leverage partition pruning
- Use Time Travel and cloning features
- Use built-in functions for traversing, flattening, and nesting semi-structured data
- Use native data types

Use data modeling to manipulate the data to meet BI requirements.
- Select and implement an effective data model
- Identify when to use a data model and when to use a flattened data set
- Use different modeling techniques for the consumption layer (for example, dimensional, Data Vault)

Optimize query performance.
- Understand the attributes of the Query Profile
- Understand how to view and analyze the query execution plan
- Troubleshoot query performance
- Leverage result, metadata, and virtual warehouse caching
- Use of different types of database objects, such as materialized views

Data Analysis

Use SQL extensibility features.
- User-Defined Functions (UDFs)
- Stored procedures
- Regular, secure, and materialized views

Perform a descriptive analysis.
- Summarize large data sets using Snowsight dashboards
- Perform exploratory ad-hoc analyses

Perform a diagnostic analysis.
- Find reasons/causes of anomalies or patterns in historical data
- Collect related data
- Identify demographics and relationships
- Analyze statistics and trends

Perform forecasting.
- Use statistics and built in functions
- Make predictions based on data

Data Presentation and Data Visualization

Given a use case, create reports and dashboards to meet business requirements.
- Evaluate and select the data for building dashboards
- Understand the effects of row access policies and Dynamic Data Masking
- Compare and contrast different chart types (for example, bar charts, scatter plots, heat grids, scorecards)
- Understand what is required to connect BI tools to Snowflake
- Create charts and dashboard in Snowsight

Given a use case, maintain reports and dashboards to meet business requirements.
- Build automated and repeatable tasks
- Operationalize data
- Store and update data
- Manage and share Snowsight dashboards
- Configure subscriptions and updates

Given a use case, incorporate visualizations for dashboards and reports.
- Present data for business use analyses
- Identify patterns and trends
- Identify correlations among variables
- Customize data presentations using filtering and editing techniques

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