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| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - 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.
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| Topic 3 | - 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.
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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q108-Q113):
NEW QUESTION # 108
You have a Fabric tenant that contains a warehouse.
You use a dataflow to load a new dataset from OneLake to the warehouse.
You need to add a Power Query step to identify the maximum values for the numeric columns.
Which function should you include in the step?
- A. Table. MaxN
- B. Table.Profile
- C. Table.Max
- D. Table.Range
Answer: B
Explanation:
The Table.Max function should be used in a Power Query step to identify the maximum values for the numeric columns. This function is designed to calculate the maximum value across each column in a table, which suits the requirement of finding maximum values for numeric columns. References = For detailed information on Power Query functions, including Table.Max, please refer to Power Query M function reference.
NEW QUESTION # 109
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 # 110
You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements. What should you do?
- A. Store all the semantic models and reports in Data Lake Gen2 storage.
- B. Modify the settings of the Research division workspaces to use an Azure Repos repository.
- C. Store all the semantic models and reports in Microsoft OneDrive.
- D. Modify the settings of the Research workspaces to use a GitHub repository.
Answer: B
Explanation:
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.
NEW QUESTION # 111
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains two schemas name schema1 and schema2 and a table named schema1.city.
You need to make a copy of schema1.city in schema2. The solution must minimize the copying of data.
Which T-SQL statement should you run?
- A. INSERT INTO schema2.city SELECT * FROM schema1.city;
- B. CREATE TABLE schema2.city AS CLONE OF schema1.city;
- C. SELECT * INTO schema2.city FROM schema1.city;
- D. CREATE TABLE schema2.city AS SELECT * FROM schema1.city;
Answer: B
Explanation:
https://learn.microsoft.com/en-us/fabric/data-warehouse/clone-table
Microsoft Fabric offers the capability to create near-instantaneous zero-copy clones with minimal storage costs.
NEW QUESTION # 112
You need to design a semantic model for the customer satisfaction report.
Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

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
NEW QUESTION # 113
......
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