Exam DP-600 Papers, Reliable DP-600 Dumps Files

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Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and maintain a data solution10-15%- Deploy data assets
  • 1. Implement CI/CD for Fabric items
  • 2. Automate deployments using APIs and scripts
  • 3. Use deployment pipelines for development to production
- Maintain a data solution
  • 1. Implement data refresh strategies
  • 2. Optimize query performance
  • 3. Manage workspace and capacity settings
Topic 2: Secure and monitor data solutions15-20%- Secure data solutions
  • 1. Configure sensitive data classifications
  • 2. Implement column-level and row-level security
  • 3. Use Microsoft Purview for data governance
  • 4. Configure workspace and item permissions
- Monitor data solutions
  • 1. Review and analyze capacity metrics
  • 2. Monitor pipeline and dataflow execution
  • 3. Implement alerting and notifications
  • 4. Use OneLake monitoring capabilities
Topic 3: Clean, transform, and enrich data25-30%- Enrich data
  • 1. Implement slowly changing dimensions (SCD)
  • 2. Merge and join data sources
  • 3. Implement incremental data loading
- Transform data
  • 1. Use Spark libraries for data transformation
  • 2. Perform schema evolution and mapping
  • 3. Implement data standardization and normalization
  • 4. Use Dataflow Gen2 for transformations
- Clean data
  • 1. Apply data cleansing techniques
  • 2. Handle missing values and duplicates
  • 3. Validate data quality using Data Quality Profiling
Topic 4: Design and manage the data model20-25%- Design a data model
  • 1. Define relationships and hierarchies
  • 2. Design a star or snowflake schema
  • 3. Choose appropriate data model type (lakehouse vs warehouse)
  • 4. Implement dimension and fact tables
- Implement and configure a data model
  • 1. Implement row-level security (RLS)
  • 2. Use DirectLake mode for large datasets
  • 3. Create and manage semantic models
  • 4. Configure SQL analytics endpoint
Topic 5: Load and prepare data20-25%- Create and configure items
  • 1. Create and configurehortcuts
  • 2. Create and configure Lakehouse, Warehouse, or data pipeline
  • 3. Configure data processing with notebooks
- Ingest data from source systems
  • 1. Use Data Factory copy activity for batch ingestion
  • 2. Use Data Factory data flow for transformation
  • 3. Configure Data Gateway for hybrid scenarios
  • 4. Implement streaming data ingestion with Eventstream
  • 5. Ingest data using PySpark or Spark SQL

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q10-Q15):

NEW QUESTION # 10
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following columns.

You create a Fabric notebook and attach it to lakehouse1.
You need to use PySpark code to transform the data. The solution must meet the following requirements:
* Add a column named pickupDate that will contain only the date portion of pickupDateTime.
* Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Add the pickupDate column: .withColumn( " pickupDate " , df[ " pickupDateTime " ].cast( " date " )) Filter the DataFrame: .filter( " fareAmount > 0 AND fareAmount < 100 " ) In PySpark, you can add a new column to a DataFrame using the .withColumn method, where the first argument is the new column name and the second argument is the expression to generate the content of the new column. Here, we use the .cast( " date " ) function to extract only the date part from a timestamp. To filter the DataFrame, you use the .filter method with a condition that selects rows where fareAmount is greater than
0 and less than 100, thus ensuring only positive values less than 100 are included.


NEW QUESTION # 11
You have the source data model shown in the following exhibit.

The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

* The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns
* The Company entity must be: Denormalized into the Customer and Product entities In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.
References =
* Dimensional modeling
* Star schema design


NEW QUESTION # 12
You have a Fabric tenant that contains three users named User1, User2, and User3. The tenant contains a security group named Group 1. User1 and User3 are members of Group"1.
The tenant contains the workspaces shown in the following table.

The tenant contains the domains shown in the following table.

User1 creates a new workspace named Workspace3.
You assign Domain! as the default domain of Group1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 13
You have an Amazon Web Services (AWS) subscription that contains an Amazon Simple Storage Service (Amazon S3) bucket named bucket1.
You have a Fabric tenant that contains a lakehouse named LH1.
In LH1, you plan to create a OneLake shortcut to bucket1.
You need to configure authentication for the connection.
Which two values should you provide? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: D,E

Explanation:
https://learn.microsoft.com/sl-si/fabric/onelake/create-s3-compatible-shortcut


NEW QUESTION # 14
You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
You are evaluating the performance of the measures.
In which use case will replacing the filter function with the keepfilters function reduce execution time?

Answer: D

Explanation:
The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.
References: This information is based on the DAX reference and performance optimization guidelines in the Microsoft Power BI documentation.


NEW QUESTION # 15
......

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