DP-600 Exam Dumps : Implementing Analytics Solutions Using Microsoft Fabric

Requirements:

Parallel processing → PySpark runs distributed across Spark clusters.

Minimize duplication of data → OneLake shortcut + Spark queries avoid unnecessary copies.

Time series analysis, anomaly detection, visualization → can be done in PySpark within Fabric Notebooks.

pandas (C) is single-threaded and not efficient for billions of items.

Power BI visuals (A) are good for visualization but not for transformation or large-scale anomaly detection.

Correct: B.

Comprehensive Detailed Explanation

From the case study:

Research division data for Productline2 is currently in CSV format in storage2.

Requirement: “All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.”

In Fabric lakehouses, managed tables are stored in Delta format inside the Tables folder.

Step 1: Reading the source

df = spark.read.format("csv") \

options(header="true", inferSchema="true") \

load("abfss://storage1.dfs.core.windows.net/files/productline2")

This correctly ingests the CSV source from ADLS Gen2.

Step 2: Writing to Lakehouse as a managed table

You must write the data in Delta format to ensure it is queryable and managed within the lakehouse.

The correct path is under Tables/, because this is where Fabric automatically manages Lakehouse managed tables.

The target table should be named productline2, so the correct path is:

df.write.mode("overwrite").format("delta").save("Tables/productline2")

Why not other options?

CSV or Parquet formats would not create a managed Lakehouse table; they would just create files.

Writing to productline2 directly (without Tables/) would store unmanaged files in the Lakehouse Files area, not managed tables.

Writing to Tables/research/productline2 adds an unnecessary subdirectory and is not the standard structure for managed tables.

Correct Final Code

df.write.mode("overwrite").format("delta").save("Tables/productline2")

References

Managed tables in Microsoft Fabric Lakehouse

Delta Lake support in Fabric

Spark write to Lakehouse

You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements.

Requirement: “All the semantic models and reports for the Research division must use version control that supports branching.”

Fabric supports Git integration (Azure Repos or GitHub) for semantic models and reports.

Storing in Data Lake Gen2 or OneDrive does not provide version control with branching.

The correct action is to integrate workspaces with a Git repository.

Answer: D. Modify the settings of the Research division workspaces to use an Azure Repos repository.