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Microsoft DP-203 exam tests the candidates' knowledge of data engineering principles, data storage options on Azure, data processing using Azure services, data transformation using Azure Databricks, and data integration using Azure Data Factory. DP-203 exam also covers topics such as data ingestion using Azure Stream Analytics, data orchestration using Azure Synapse Analytics, and data security and compliance.
NEW QUESTION # 136
You plan to create an Azure Data Lake Storage Gen2 account
You need to recommend a storage solution that meets the following requirements:
* Provides the highest degree of data resiliency
* Ensures that content remains available for writes if a primary data center fails What should you include in the recommendation? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
See the answer in explanation.
Explanation
answer is below
NEW QUESTION # 137
You are implementing an Azure Stream Analytics solution to process event data from devices.
The devices output events when there is a fault and emit a repeat of the event every five seconds until the fault is resolved. The devices output a heartbeat event every five seconds after a previous event if there are no faults present.
A sample of the events is shown in the following table.
You need to calculate the uptime between the faults.
How should you complete the Stream Analytics SQL query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
Box 1: WHERE EventType='HeartBeat'
Box 2: ,TumblingWindow(Second, 5)
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.
The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.
Timeline Description automatically generated
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/session-window-azure-stream-analytics
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
NEW QUESTION # 138
You store files in an Azure Data Lake Storage Gen2 container. The container has the storage policy shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection Is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/dotnet/api/microsoft.azure.management.storage.fluent.models.managementpolicybaseblob.tiertocool
NEW QUESTION # 139
You are creating dimensions for a data warehouse in an Azure Synapse Analytics dedicated SQL pool.
You create a table by using the Transact-SQL statement shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/populate-slowly-changing-dimensions-azure-synapse-analytics-pipelines/3-choose-between-dimension-types
NEW QUESTION # 140
You have the following Azure Data Factory pipelines
* ingest Data from System 1
* Ingest Data from System2
* Populate Dimensions
* Populate facts
ingest Data from System1 and Ingest Data from System1 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System* Populate Facts must execute after the Populate Dimensions pipeline. All the pipelines must execute every eight hours.
What should you do to schedule the pipelines for execution?
- A. Create a parent pipeline that contains the four pipelines and use an event trigger.
- B. Add an event trigger to all four pipelines.
- C. Add a schedule trigger to all four pipelines.
- D. Create a parent pipeline that contains the four pipelines and use a schedule trigger.
Answer: D
Explanation:
Schedule trigger: A trigger that invokes a pipeline on a wall-clock schedule.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
NEW QUESTION # 141
You use Azure Data Lake Storage Gen2 to store data that data scientists and data engineers will query by using Azure Databricks interactive notebooks. Users will have access only to the Data Lake Storage folders that relate to the projects on which they work.
You need to recommend which authentication methods to use for Databricks and Data Lake Storage to provide the users with the appropriate access. The solution must minimize administrative effort and development effort.
Which authentication method should you recommend for each Azure service? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Table Description automatically generated
Box 1: Personal access tokens
You can use storage shared access signatures (SAS) to access an Azure Data Lake Storage Gen2 storage account directly. With SAS, you can restrict access to a storage account using temporary tokens with fine-grained access control.
You can add multiple storage accounts and configure respective SAS token providers in the same Spark session.
Box 2: Azure Active Directory credential passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 (ADLS Gen1) and Azure Data Lake Storage Gen2 (ADLS Gen2) from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
After configuring Azure Data Lake Storage credential passthrough and creating storage containers, you can access data directly in Azure Data Lake Storage Gen1 using an adl:// path and Azure Data Lake Storage Gen2 using an abfss:// path:
Reference:
https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/azure/adls-gen2/azure-datalake-gen2-sas-acc
https://docs.microsoft.com/en-us/azure/databricks/security/credential-passthrough/adls-passthrough
NEW QUESTION # 142
A company plans to use Platform-as-a-Service (PaaS) to create the new data pipeline process. The process must meet the following requirements:
Ingest:
Access multiple data sources.
Provide the ability to orchestrate workflow.
Provide the capability to run SQL Server Integration Services packages.
Store:
Optimize storage for big data workloads.
Provide encryption of data at rest.
Operate with no size limits.
Prepare and Train:
Provide a fully-managed and interactive workspace for exploration and visualization.
Provide the ability to program in R, SQL, Python, Scala, and Java.
Provide seamless user authentication with Azure Active Directory.
Model & Serve:
Implement native columnar storage.
Support for the SQL language
Provide support for structured streaming.
You need to build the data integration pipeline.
Which technologies should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 143
You are designing a monitoring solution for a fleet of 500 vehicles. Each vehicle has a GPS tracking device that sends data to an Azure event hub once per minute.
You have a CSV file in an Azure Data Lake Storage Gen2 container. The file maintains the expected geographical area in which each vehicle should be.
You need to ensure that when a GPS position is outside the expected area, a message is added to another event hub for processing within 30 seconds. The solution must minimize cost.
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
Box 1: Azure Stream Analytics
Box 2: Hopping
Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap and be emitted more often than the window size. Events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.
Box 3: Point within polygon
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
NEW QUESTION # 144
that has the activity shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
Answer:
Explanation:
Explanation:
NEW QUESTION # 145
You are performing exploratory analysis of the bus fare data in an Azure Data Lake Storage Gen2 account by using an Azure Synapse Analytics serverless SQL pool.
You execute the Transact-SQL query shown in the following exhibit.
What do the query results include?
- A. All files that have file names that beginning with "tripdata_2020".
- B. All CSV files that have file names that contain "tripdata_2020".
- C. Only CSV files in the tripdata_2020 subfolder.
- D. Only CSV that have file names that beginning with "tripdata_2020".
Answer: D
NEW QUESTION # 146
You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.
How should you configure the new cluster? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: High Concurrency
Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.
Incorrect:
Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.
Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.
Box 2: Azure Data Lake Storage Gen1 Credential Passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
References:
https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html
NEW QUESTION # 147
You need to implement a Type 3 slowly changing dimension (SCD) for product category data in an Azure Synapse Analytics dedicated SQL pool.
You have a table that was created by using the following Transact-SQL statement.
Which two columns should you add to the table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. [OriginalProduccCacegory] [nvarchar] (100) NOT NULL,
- B. [EffectiveScarcDate] [datetime] NOT NULL,
- C. [ProductCategory] [nvarchar] (100) NOT NULL,
- D. [CurrentProduccCacegory] [nvarchar] (100) NOT NULL,
- E. [EffectiveEndDace] [dacecime] NULL,
Answer: A,D
Explanation:
A Type 3 SCD supports storing two versions of a dimension member as separate columns. The table includes a column for the current value of a member plus either the original or previous value of the member. So Type 3 uses additional columns to track one key instance of history, rather than storing additional rows to track each change like in a Type 2 SCD.
This type of tracking may be used for one or two columns in a dimension table. It is not common to use it for many members of the same table. It is often used in combination with Type 1 or Type 2 members.
Reference:
https://k21academy.com/microsoft-azure/azure-data-engineer-dp203-q-a-day-2-live-session-review/
NEW QUESTION # 148
You have an Azure Synapse Analytics workspace named WS1.
You have an Azure Data Lake Storage Gen2 container that contains JSON-formatted files in the following format.
You need to use the serverless SQL pool in WS1 to read the files.
How should you complete the Transact-SQL statement? To answer, drag the appropriate values to 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.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-single-csv-file
https://docs.microsoft.com/en-us/sql/relational-databases/json/import-json-documents-into-sql-server
NEW QUESTION # 149
You have an Azure Data Lake Storage Gen2 container.
Data is ingested into the container, and then transformed by a data integration application. The data is NOT modified after that. Users can read files in the container but cannot modify the files.
You need to design a data archiving solution that meets the following requirements:
* New data is accessed frequently and must be available as quickly as possible.
* Data that is older than five years is accessed infrequently but must be available within one second when requested.
* Data that is older than seven years is NOT accessed. After seven years, the data must be persisted at the lowest cost possible.
* Costs must be minimized while maintaining the required availability.
How should you manage the data? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Explanation:
Box 1: Move to cool storage
Box 2: Move to archive storage
Archive - Optimized for storing data that is rarely accessed and stored for at least 180 days with flexible latency requirements, on the order of hours.
The following table shows a comparison of premium performance block blob storage, and the hot, cool, and archive access tiers.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-storage-tiers Explanation:
Box 1: Replicated
Replicated tables are ideal for small star-schema dimension tables, because the fact table is often distributed on a column that is not compatible with the connected dimension tables. If this case applies to your schema, consider changing small dimension tables currently implemented as round-robin to replicated.
Box 2: Replicated
Box 3: Replicated
Box 4: Hash-distributed
For Fact tables use hash-distribution with clustered columnstore index. Performance improves when two hash tables are joined on the same distribution column.
Reference:
https://azure.microsoft.com/en-us/updates/reduce-data-movement-and-make-your-queries-more-efficient-with-th
https://azure.microsoft.com/en-us/blog/replicated-tables-now-generally-available-in-azure-sql-data-warehouse/
NEW QUESTION # 150
You are responsible for providing access to an Azure Data Lake Storage Gen2 account.
Your user account has contributor access to the storage account, and you have the application ID and access key.
You plan to use PolyBase to load data into an enterprise data warehouse in Azure Synapse Analytics.
You need to configure PolyBase to connect the data warehouse to storage account.
Which three components should you create in sequence? To answer, move the appropriate components from the list of components to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
NEW QUESTION # 151
You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
Status: Running
Type: Self-Hosted
Running / Registered Node(s): 1/1
High Availability Enabled: False
Linked Count: 0
Queue Length: 0
Average Queue Duration. 0.00s
The integration runtime has the following node details:
Name: X-M
Status: Running
Available Memory: 7697MB
CPU Utilization: 6%
Network (In/Out): 1.21KBps/0.83KBps
Concurrent Jobs (Running/Limit): 2/14
Role: Dispatcher/Worker
Credential Status: In Sync
Use the drop-down menus to select the answer choice that completes each statement based on the information presented.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime
NEW QUESTION # 152
What should you do to improve high availability of the real-time data processing solution?
- A. Set Data Lake Storage to use geo-redundant storage (GRS).
- B. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
- C. Deploy a High Concurrency Databricks cluster.
- D. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
Answer: B
Explanation:
Guarantee Stream Analytics job reliability during service updates
Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure's paired region model.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability
NEW QUESTION # 153
You have an Azure data factory that has the Git repository settings shown in the following exhibit.
Use the drop-down menus to select the answer choose that completes each statement based on the information presented in the graphic.
NOTE: Each correct answer is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 154
You plan to develop a dataset named Purchases by using Azure databricks Purchases will contain the following columns:
* ProductID
* ItemPrice
* lineTotal
* Quantity
* StorelD
* Minute
* Month
* Hour
* Year
* Day
You need to store the data to support hourly incremental load pipelines that will vary for each StoreID. the solution must minimize storage costs. How should you complete the rode? To answer, select the appropriate options In the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: partitionBy
We should overwrite at the partition level.
Example:
df.write.partitionBy("y","m","d")
mode(SaveMode.Append)
parquet("/data/hive/warehouse/db_name.db/" + tableName)
Box 2: ("StoreID", "Year", "Month", "Day", "Hour", "StoreID")
Box 3: parquet("/Purchases")
Reference:
https://intellipaat.com/community/11744/how-to-partition-and-write-dataframe-in-spark-without-deleting-partitio
NEW QUESTION # 155
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Microsoft DP-203 certification exam is a challenging exam that requires extensive preparation and study. Candidates can prepare for the exam by taking online courses, attending training sessions, and studying the official Microsoft DP-203 certification exam guide. DP-203 exam is conducted online and consists of multiple-choice questions and scenario-based questions. Candidates must score a minimum of 700 out of 1000 to pass the exam and earn the Microsoft DP-203 certification.
To prepare for the DP-203 exam, candidates should have a solid understanding of Azure services and be familiar with programming languages such as Python and SQL. They should also have experience working with data storage solutions such as Azure Blob Storage and Azure Data Lake Storage. Microsoft offers a variety of training resources to help candidates prepare for the exam, including online courses, instructor-led training, and study guides.
New Microsoft DP-203 Dumps & Questions: https://theexamcerts.lead2passexam.com/Microsoft/valid-DP-203-exam-dumps.html