Pass the Microsoft Microsoft Certified: Fabric Analytics Engineer Associate DP-600 Questions and answers with CertsForce

To implement row-level security (RLS) in a Fabric Warehouse (like in Azure Synapse or SQL Server):

You must define a predicate function (usually an inline table-valued function) that filters rows for each user → Function.

Then you bind that function to the table using a Security Policy → Security Policy.

A Database Role is used for group-based access control but not specifically for implementing RLS.

Stored procedures and constraints are not used for RLS.

Correct answers: A and D

The charts in the Fabric notebook are summarizing customer churn data (retained vs lost customers) across dimensions such as geography, products purchased, age, and tenure. This is describing " what has happened " in the data.

Descriptive analytics summarizes and visualizes historical data.

Diagnostic analytics explains " why " events occurred.

Predictive analytics forecasts future outcomes.

Prescriptive analytics recommends actions.

Since only distributions are being shown, this is descriptive analytics.

In Microsoft Fabric, a shortcut lets you reference data from another workspace’s Lakehouse or external storage without duplicating the data.

A view would only apply within the same database/Lakehouse, not across workspaces.

A dataflow transforms and loads data but creates a new copy.

A managed table is a physical table stored in the Lakehouse itself, not a cross-reference.

So, to query dbo.Sales (Lakehouse1) and dbo.Customers (Lakehouse2) in a single SQL query, you use shortcuts.

Comprehensive Detailed Explanation

When assigning permissions to roles in a Fabric workspace for analytics workloads, you must align permissions with the responsibilities and tools typically used by each persona:

1. Data Engineers

Their primary tasks include building and transforming data pipelines, as well as making datasets usable by others.

The appropriate permission is Build Reports on the default dataset, because this allows them to create and manage reports using the curated semantic model while preparing data for analysts and scientists.

2. Data Analysts

Analysts work heavily with Apache Spark for exploratory analysis, cleansing, and shaping data.

The correct permission is Read All Apache Spark, as this grants them the ability to query and analyze Spark-based data directly without elevated permissions that are unnecessary for their role.

3. Data Scientists

Data scientists often use SQL analytics endpoints to run queries, train models, and integrate data into machine learning workflows.

The correct permission is Read All SQL analytics endpoint data, since it enables direct programmatic access to the SQL endpoint, which is needed for advanced modeling and experimentation.

Summary of Selections:

DataEngineers → Build Reports on the default dataset

DataAnalysts → Read All Apache Spark

DataScientists → Read All SQL analytics endpoint data

References (Microsoft Fabric – DP-600 exam scope):

Workspace roles and permissions in Microsoft Fabric

SQL analytics endpoint in Fabric

Apache Spark in Microsoft Fabric

A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. References = For further understanding, Microsoft ' s documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

When using Power BI Project (.pbip) with Git integration:

The .pbip project separates model metadata (schema, measures, relationships, etc.) from actual data values.

The localSettings.json file stores machine-specific and environment-specific settings, including data source credentials and local configurations. These should not be checked into version control to avoid exposing sensitive information or environment-specific configs.

Other options:

unappliedChanges.json → tracks unsaved changes, not data values.

model.bim → defines the semantic model schema (measures, relationships, metadata) and must be included in version control.

cache.abf → stores Analysis Services cache data, not part of source control.

Therefore, the correct file to exclude is localSettings.json.

The Vertipaq Analyzer tool (B) and querying the $system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. References = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.

Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.