Free Practice Questions for Snowflake DAA-C01 Certification

● Retrieve data from a source ○ Structured (CSV) ○ Semi-structured (e.g., Parquet, Avro, ORC, JSON, or XML) ○ Unstructured ○ Synthetic Data Generation

● Query tables in Snowflake to assess: ○ Data elements including statistics maintained by Snowflake ○ The elements that are required for business goals (using BI reports or SQL analysis) ○ The level of data granularity required ● Evaluate which transformations are required: ○ Perform table joins and set operations (e.g., UNION, UNION ALL, INTERSECT, and MINUS) ○ Perform data filtering and/or transformation ○ ASOF JOINS ● Use commands to read metadata and/or to alter context (e.g., DESCRIBE, SHOW, USE)

● Find external data sets that correlate with available data ● Use Secure Data Sharing to enrich existing data sets (e.g., Data from Snowflake Marketplace, The Internal Marketplace, Private Listings, and Listings) ● Create tables and views

● Define primary keys for tables ● Perform table joins between parent/child tables ○ Implement constraints

● Cleanse, conform, and enrich data ● Automate and implement data pipelines ○ Scheduling ● Respond to processing failures ○ Use logging and monitoring solutions ○ Auditing ○ Data lineage

● Load files using Snowsight ● Load data from external/internal stages into a table ● Load different types of data ○ Tabular data/structured data ○ Semi-structured data ○ Unstructured data ● Perform general DML (INSERT, UPDATE, and DELETE) ● Identify and resolve data import errors ● Prepare external tables

● Scalar functions ● Aggregate functions ● Window functions ● Table functions ● System functions ● Geospatial functions ● AI functions ● User-Defined Functions (UDFs) ● ML functions ○ Classification ○ Top Insights ○ Anomaly Detection

● CSV ● JSON (query and parse) ● Parquet ● XML

● Identify and analyze data quality issues ● Handle erroneous and ambiguous data ○ Handle duplications ○ Handle nulls ● Convert data types ● Use clones as required by specific use-cases ● Use Data Metric Functions (DMFs) ● Use Time Travel and cloning features ● Use built-in functions for traversing, flattening, transforming, and nesting semi-structured data ● Use native data types

● Aggregate and validate the data ● Apply analytic/window functions ● Perform pre-math calculations (e.g., randomization, ranking, grouping, min/max) ● Perform casting - change data types to ensure data can be presented consistently ● Enrich the data ○ Use cartesian joins, sub-queries, CTEs, and union queries ○ Work with hierarchical data ○ Use sampling, approximation, and estimation features

● 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 (e.g., dimensional, Data Vault)

● Understand how to view and analyze the query execution plan ● Troubleshoot query performance ○ Leverage partition pruning ○ Leverage clustering keys ● Leverage result, metadata, and virtual warehouse caching ● Use search optimization service and virtual warehouse features such as the query acceleration services

● User-Defined Functions (UDFs) ● User-Defined Table Functions (UDTFs) ● Stored procedures ○ Asynchronous Stored Procedure ● Regular, secure, and materialized views

● Summarize large data sets using Snowsight dashboards ○ Create a reusable filter ● Perform exploratory ad-hoc analyses using Notebooks and worksheets to describe data

● Find reasons/causes of anomalies or patterns in historical data ● Collect related data ● Identify demographics and relationships ● Analyze statistics and trends

● Use statistics and built-in functions ● Make predictions based on data

● Evaluate and select the data for building dashboards ○ Set the contexts (e.g., database, schema, virtual warehouse, role) ○ Create and run SQL queries ○ Apply naming conventions to data columns and queries ○ Sort and filter data ● Understand the effects of row access policies and Dynamic Data Masking ● Compare and contrast different chart types (e.g., bar charts, scatter plots, heat grids, scorecards) ● Understand what is required to connect BI tools to Snowflake ● Create charts and dashboard in Snowsight ○ Create and manage custom filters

● Build automated and repeatable tasks ● Operationalize data for consumption ● Manage and share Snowsight dashboards ● Configure subscriptions and updates

● Present data for business-use analyses ● Identify patterns and trends ● Identify correlations among variables ● Troubleshoot common issues with data analytics dashboard and reports ● Customize data presentations using filtering and editing techniques