HOTSPOT (Drag and Drop is not supported)You are building an Azure Stream Analytics job to retrieve game data.You need to ensure that the job returns the highest scoring record for each five-minute time interval of each game.How should you complete the Stream Analytics query? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point. Hot Area:
Answer(s): A
Box 1: TopOne OVER(PARTITION BY Game ORDER BY Score Desc)TopOne returns the top-rank record, where rank defines the ranking position of the event in the window according to the specified ordering. Ordering/ranking is based on event columns and can be specified in ORDER BY clause.Box 2: Hopping(minute,5)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.
https://docs.microsoft.com/en-us/stream-analytics-query/topone-azure-stream-analytics https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You have an Azure Data Lake Storage account that contains a staging zone.You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics. Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that copies the data to a staging table in the data warehouse, and then uses a stored procedure to execute the R script.Does this meet the goal?
Answer(s): B
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads: -A workload for data engineers who will use Python and SQL.-A workload for jobs that will run notebooks that use Python, Scala, and SQL.-A workload that data scientists will use to perform ad hoc analysis in Scala and R.The enterprise architecture team at your company identifies the following standards for Databricks environments: -The data engineers must share a cluster. -The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.-All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.You need to create the Databricks clusters for the workloads.Solution: You create a High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.Does this meet the goal?
Need a High Concurrency cluster for the jobs.Standard clusters are recommended for a single user. Standard can run workloads developed in any language: Python, R, Scala, and SQL.A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine- grained sharing for maximum resource utilization and minimum query latencies.
https://docs.azuredatabricks.net/clusters/configure.html
You are designing an Azure Databricks cluster that runs user-defined local processes. You need to recommend a cluster configuration that meets the following requirements: Minimize query latency.Maximize the number of users that can run queries on the cluster at the same time. Reduce overall costs without compromising other requirements.Which cluster type should you recommend?
A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies.Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don?t need to provision the cluster to match a workload.Incorrect Answers:C: The cluster configuration includes an auto terminate setting whose default value depends on cluster mode: Standard and Single Node clusters terminate automatically after 120 minutes by default.High Concurrency clusters do not terminate automatically by default.
https://docs.microsoft.com/en-us/azure/databricks/clusters/configure
HOTSPOT (Drag and Drop is not supported)You are building an Azure Data Factory solution to process data received from Azure Event Hubs, and then ingested into an Azure Data Lake Storage Gen2 container.The data will be ingested every five minutes from devices into JSON files. The files have the following naming pattern./{deviceType}/in/{YYYY}/{MM}/{DD}/{HH}/{deviceID}_{YYYY}{MM}{DD}HH}{mm}.jsonYou need to prepare the data for batch data processing so that there is one dataset per hour per deviceType. The solution must minimize read times.How should you configure the sink for the copy activity? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point. Hot Area:
Box 1: @trigger().startTimestartTime: A date-time value. For basic schedules, the value of the startTime property applies to the first occurrence. For complex schedules, the trigger starts no sooner than the specified startTime value.Box 2: /{YYYY}/{MM}/{DD}/{HH}_{deviceType}.json One dataset per hour per deviceType.Box 3: Flatten hierarchy- FlattenHierarchy: All files from the source folder are in the first level of the target folder. The target files have autogenerated names.
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers https://docs.microsoft.com/en-us/azure/data-factory/connector-file-system
Post your Comments and Discuss Microsoft DP-203 exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the DP-203 content, but please register or login to continue.