DP-600 Exam Dumps - Microsoft Certified: Fabric Analytics Engineer Associate Questions and Answers

The Table.Profile function in Power Query is used to generate column statistics such as count, average, null count, distinct count, and standard deviation. You can use this function as follows:

Invoke the Power Query Editor.

Apply the Table.Profile function to your table.

The result will be a table where each row represents a column from the original table, and each column in the result represents a different statistic such as those listed in the requirement.

We are analyzing the query:

SELECT e.[WWI Employee ID],

e.Employee,

e.[Preferred Name],

gdr.[WWI Employee ID] AS [Direct Report ID] ,

gdr.Employee AS [Direct Report]

FROM Dimension.Employee AS e

OUTER APPLY Dimension.GetDirectReports(e.[Employee Key]) AS gdr;

Key concepts:

APPLY operator: Used to join a table with a table-valued function.

OUTER APPLY ensures all rows from the left (Employee) are returned, even if the function returns no rows (similar to LEFT JOIN).

Table-valued function (TVF): Returns a set of rows (unlike scalar functions, which return a single value).

The function executes once per row from the left table, not once for the whole query.

Evaluating each statement:

" Dimension.GetDirectReports is a scalar T-SQL function. "

No → It must be a table-valued function, because OUTER APPLY requires a table expression.

" The Dimension.GetDirectReports function will run only once when the query runs. "

No → With APPLY, the function runs for each row of Dimension.Employee.

" The output rows will include at least one row for each row in the Dimension.Employee table. "

Yes → Because of OUTER APPLY, every employee row is included, even if there are no direct reports (in that case, the columns from gdr will be NULL).

Final Answer:

Scalar function → No

Runs once per query → No

At least one row per Employee → Yes

The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user ' s credentials.

Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

Authentication method: Service principal authentication

Mode: DirectQuery

The measure should use the AVERAGE function to calculate the average value.

It should reference the Response Value column from the ' Survey ' table.

The ' Number of months ' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

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.

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.

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