Free Microsoft DP-700 Exam Dumps Questions & Answers

DRAG DROP
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData.
The following is a sample of the data in TaxiData.

You need to build two KQL queries. The solution must meet the following requirements:
One of the queries must partition RunningTotalAmount by VendorID.
The other query must create a column named FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
How should you complete each query? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

Partition the RunningTotalAmount by VendorID. - Row_cumsum
The Row_cumsum function computes the cumulative sum of a column while optionally restarting the accumulation based on a condition. In this case, it calculates the cumulative sum of total_amount for each VendorID, restarting when the VendorID changes (VendorID != prev(VendorID)).

Create a column FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime, partitioned by payment_type - Row_window_session

You are building a Fabric notebook named MasterNotebookl in a workspace. MasterNotebookl contains the following code.

You need to ensure that the notebooks are executed in the following sequence:
1. Notebook_03
2. Notebook.Ol
3. Notebook_02
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A.Change the concurrency to 3.
• B.Add dependencies to the execution of Notebook_03.
• C.Split the Directed Acyclic Graph (DAG) definition into three separate definitions.
• D.Add dependencies to the execution of Note boo k_02.
• E.Move the declaration of Notebook_02 to the bottom of the Directed Acyclic Graph (DAG) definition.
• F.Move the declaration of Notebook_03 to the top of the Directed Acyclic Graph (DAG) definition.

You have a Fabric warehouse named DW1 that contains a Type 2 slowly changing dimension (SCD) dimension table named DimCustomer. DimCustomer contains 100 columns and 20 million rows. The columns are of various data types, including int, varchar, date, and varbinary.
You need to identify incoming changes to the table and update the records when there is a change. The solution must minimize resource consumption.
What should you use to identify changes to attributes?

• A.a direct attributes comparison for the attributes in the source table.
• B.a hash function to compare the attributes in the DimCustomer table.
• C.a direct attributes comparison across the attributes in the DimCustomer table.
• D.a hash function to compare the attributes in the source table.

Your company has a team of developers. The team creates Python libraries of reusable code that is used to transform data.
You create a Fabric workspace name Workspace1 that will be used to develop extract, transform, and load (ETL) solutions by using notebooks.
You need to ensure that the libraries are available by default to new notebooks in Workspace1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct Answer:

Explanation:

Topic 1, Litware, IncCase Study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview
Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide.
Litware also manages an online advertising business for the authors it represents.
Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.
Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse.
Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled.
Litware has i mag e files of book covers in Azure Blob Storage. The files are loaded into the Files folder.
Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source:
Sales Date
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: "The SQL query failed while running." The data engineering team wants to debug the issue and find queries that cause more than one failure.
When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process.
The company's sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.

You have a Fabric workspace that contains a lakehouse named Lakehousel.
You plan to create a data pipeline named Pipeline! to ingest data into Lakehousel. You will use a parameter named paraml to pass an external value into Pipeline1!. The paraml parameter has a data type of int You need to ensure that the pipeline expression returns param1 as an int value.
How should you specify the parameter value?

• A." @{pipeline().parameters.paraml}-
• B." @{pipeline().parameters.[paraml]} "
• C." @{pipeline().parameters.paraml} "
• D." @pipeline(). parameters. paraml "