Free Practice Questions for Microsoft Certified: Azure Enterprise Data Analyst Associate (DP-750) Certification

- Choose an appropriate compute type, including job compute, serverless, warehouse, classic compute, and shared compute - Configure compute performance settings, including CPU, node count, autoscaling, termination, node type, cluster size, and pooling - Configure compute feature settings, including Photon acceleration, Azure Databricks runtime/Spark version, and machine learning - Install libraries for a compute resource - Configure access permissions to a compute resource

- Apply naming conventions based on requirements, including isolation, development environment, and external sharing - Create a catalog based on requirements - Create a schema based on requirements - Create volumes based on requirements - Create tables, views, and materialized views - Implement a foreign catalog by configuring connections - Implement data definition language (DDL) operations on managed and external tables - Configure AI/BI Genie instructions for data discovery

- Grant privileges to a principal (user, service principal, or group) for securable objects in Unity Catalog - Implement table- and column-level access control and row-level security - Access Azure Key Vault secrets from within Azure Databricks - Authenticate data access by using service principals - Authenticate resource access by using managed identities

- Create, implement, and preserve table and column definitions and descriptions for data discovery - Configure attribute-based access control (ABAC) by using tags and policies - Configure row filters and column masks - Apply data retention policies - Set up and manage data lineage tracking by using Catalog Explorer, including owner, history, dependencies, and lineage - Configure audit logging - Design and implement a secure strategy for Delta Sharing

- Design logic for data ingestion and data source configuration, including extraction type and file type - Choose an appropriate data ingestion tool, including Lakeflow Connect, notebooks, and Azure Data Factory - Choose a data loading method, including batch and streaming - Choose a data table format, such as Parquet, Delta, CSV, JSON, or Iceberg - Design and implement a data partitioning scheme - Choose a slowly changing dimension (SCD) type - Choose granularity on a column or table based on requirements - Design and implement a temporal (history) table to record changes over time - Design and implement a clustering strategy, including liquid clustering, Z-ordering, and deletion vectors - Choose between managed and unmanaged tables

- Ingest data by using Lakeflow Connect, including batch and streaming - Ingest data by using notebooks, including batch and streaming - Ingest data by using SQL methods, including CREATE TABLE … AS (CTAS), CREATE OR REPLACE TABLE, and COPY INTO - Ingest data by using a change data capture (CDC) feed - Ingest data by using Spark Structured Streaming - Ingest streaming data from Azure Event Hubs - Ingest data by using Lakeflow Spark Declarative Pipelines, including Auto Loader

- Profile data to generate summary statistics and assess data distributions - Choose appropriate column data types - Identify and resolve duplicate, missing, and null values - Transform data, including filtering, grouping, and aggregating data - Transform data by using join, union, intersect, and except operators - Transform data by denormalizing, pivoting, and unpivoting data - Load data by using merge, insert, and append operations

- Implement validation checks, including nullability, data cardinality, and range checking - Implement data type checks - Implement schema enforcement and manage schema drift - Manage data quality with pipeline expectations in Lakeflow Spark Declarative Pipelines

- Design order of operations for a data pipeline - Choose between notebook and Lakeflow Spark Declarative Pipelines - Design task logic for Lakeflow Jobs - Design and implement error handling in data pipelines, notebooks, and jobs - Create a data pipeline by using a notebook, including precedence constraints - Create a data pipeline by using Lakeflow Spark Declarative Pipelines

- Create a job, including setup and configuration - Configure job triggers - Schedule a job - Configure alerts for a job - Configure automatic restarts for a job or a data pipeline

- Apply version control best practices using Git - Manage branching, pull requests, and conflict resolution - Implement a testing strategy, including unit tests, integration tests, end-to-end tests, and user acceptance testing (UAT) - Configure and package Databricks Asset Bundles - Deploy a bundle by using the Azure Databricks command-line interface (CLI) - Deploy a bundle by using REST APIs

- Monitor and manage cluster consumption to optimize performance and cost - Troubleshoot and repair issues in Lakeflow Jobs, including repair, restart, stop, and run functions - Troubleshoot and repair issues in Apache Spark jobs and notebooks, including performance tuning, resolving resource bottlenecks, and cluster restart - Investigate and resolve caching, skewing, spilling, and shuffle issues by using a Directed Acyclic Graph (DAG), the Spark UI, and query profile - Optimize Delta tables for performance and cost, including OPTIMIZE and VACUUM commands - Implement log streaming by using Log Analytics in Azure Monitor - Configure alerts by using Azure Monitor