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Microsoft DP-600 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Analytics Solutions Using Microsoft Fabric
Exam Number:DP-600
Real Exam Qty:40–60
Exam Duration:100 minutes
Exam Price:USD 165
Exam Format:Multiple choice, Case study, Scenario-based
Passing Score:700 / 1000
Certificate Validity Period:2 years
Available Languages:Portuguese (Brazil), French, Spanish, Chinese (Simplified), English, Japanese, German
Recommended Training:Microsoft Learn: DP-600 Learning Path
Exam Registration:Microsoft Certification Exam Registration
Sample Questions:Microsoft DP-600 Sample Questions
Exam Way:Online proctored or onsite at authorized test centers
Pre Condition:No mandatory prerequisites; recommended experience with data modeling, SQL, DAX, and Microsoft Fabric components
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-600

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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement and manage semantic models: This section of the exam measures the skills of architects and focuses on designing and optimizing semantic models to support enterprise-scale analytics. It evaluates understanding of storage modes and implementing star schemas and complex relationships, such as bridge tables and many-to-many joins. Architects must write DAX-based calculations using variables, iterators, and filtering techniques. The use of calculation groups, dynamic format strings, and field parameters is included. The section also includes configuring large semantic models and designing composite models. For optimization, candidates are expected to improve report visual and DAX performance, configure Direct Lake behaviors, and implement incremental refresh strategies effectively.
Topic 2
  • Prepare data: This section of the exam measures the skills of engineers and covers essential data preparation tasks. It includes establishing data connections and discovering sources through tools like the OneLake data hub and the real-time hub. Candidates must demonstrate knowledge of selecting the appropriate storage type—lakehouse, warehouse, or eventhouse—depending on the use case. It also includes implementing OneLake integrations with Eventhouse and semantic models. The transformation part involves creating views, stored procedures, and functions, as well as enriching, merging, denormalizing, and aggregating data. Engineers are also expected to handle data quality issues like duplicates, missing values, and nulls, along with converting data types and filtering. Furthermore, querying and analyzing data using tools like SQL, KQL, and the Visual Query Editor is tested in this domain.
Topic 3
  • Maintain a data analytics solution: This section of the exam measures the skills of administrators and covers tasks related to enforcing security and managing the Power BI environment. It involves setting up access controls at both workspace and item levels, ensuring appropriate permissions for users and groups. Row-level, column-level, object-level, and file-level access controls are also included, alongside the application of sensitivity labels to classify data securely. This section also tests the ability to endorse Power BI items for organizational use and oversee the complete development lifecycle of analytics assets by configuring version control, managing Power BI Desktop projects, setting up deployment pipelines, assessing downstream impacts from various data assets, and handling semantic model deployments using XMLA endpoint. Reusable asset management is also a part of this domain.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q85-Q90):

NEW QUESTION # 85
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.

Which three Data view options should you select? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

Answer: A,B,D

Explanation:
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details.
These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.


NEW QUESTION # 86
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?

Answer: C

Explanation:
The display function is the correct choice to make the data available in the Chart view within a Fabric notebook. This function is used to visualize Spark DataFrames in various formats including charts and graphs directly within the notebook environment. References = Further explanation of the display function can be found in the official documentation on Azure Synapse Analytics notebooks.


NEW QUESTION # 87
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.

Which three Data view options should you select? Each correct answer presents part of the solution. NOTE:
Each correct answer is worth one point.

Answer: A,B,D

Explanation:
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.
References: These features and their descriptions are typically found in the Power Query documentation, under the section for data profiling and quality features.


NEW QUESTION # 88
You have a Fabric tenant that contains a data pipeline.
You need to ensure that the pipeline runs every four hours on Mondays and Fridays.
To what should you set Repeat for the schedule?

Answer: D

Explanation:
The only way to do this is to set the schedule to ''Weekly'', set the days on Monday and Friday and add manually 6 Time of 4 hour intervals.


NEW QUESTION # 89
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData that stores the following data.

You need to create a column named FirstPickupDateTime that will contain the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Comprehensive Detailed Explanation
We have a KQL table (TaxiData) with columns:
VendorID
tpep_pickup_datetime (timestamp)
payment_type
total_amount
The requirement:
Create a new column FirstPickupDateTime
It should contain the first pickup timestamp per hour
Partitioning should be done by payment_type
Step 1: Which windowing function?
row_cumsum # running cumulative sum (not needed here).
row_rank_dense # assigns ranks without gaps, but does not guarantee minimum value only.
row_rank_min # gives the first/minimum value in each window partition. # Correct.
row_window_session # sessionization of events, not required.
So, the correct function is row_rank_min .
Step 2: Which comparison operator?
We need to select the row where the rank = 1 (the first per partition).
So the correct operator is == (equals).
Step 3: Partitioning
The KQL query should partition by:
bin(tpep_pickup_datetime, 1h) # buckets data into 1-hour windows
payment_type # partitions further by payment type
Completed KQL Query
TaxiData
| sort by tpep_pickup_datetime asc, payment_type asc
| extend FirstPickupDateTime = row_rank_min(tpep_pickup_datetime, 1h, 0m, payment_type)
| where FirstPickupDateTime == 1
This assigns a rank within each 1-hour, per-payment-type window, then keeps the first pickup timestamp .
Why This Works
row_rank_min # ensures we capture the first occurrence in each hour.
== # filters only the first row per partition.
bin(..., 1h) ensures grouping is by hour.
References
Kusto row_rank_min() function
KQL window functions


NEW QUESTION # 90
......

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