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You plan to perform batch processing in Azure Databricks once daily. Which type of Databricks cluster should you use?
Answer: B
There are 2 types of databricks clusters: 1) Standard/Interactive - best for querying and processing data by users. 2) Automatic/Jobs - best for jobs and automated batch processing.
Source: Adapted from community discussion — wijaz789
HOTSPOT - You configure version control for an Azure Data Factory instance as 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. Hot Area:
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
Box 1: adf_publish - The Publish branch is the branch in your repository where publishing related ARM templates are stored and updated. By default, it's adf_publish. Box 2: / dwh_batchetl/adf_publish/contososales Note: RepositoryName (here dwh_batchetl): Your Azure Repos code repository name. Azure Repos projects contain Git repositories to manage your source code as your project grows. You can create a new repository or use an existing repository that's already in your project. Reference: https://docs.microsoft.com/en-us/azure/data-factory/source-control
Source: Adapted from community discussion — varmal
HOTSPOT - You are designing an Azure Stream Analytics solution that receives instant messaging data from an Azure Event Hub. You need to ensure that the output from the Stream Analytics job counts the number of messages per time zone every 15 seconds. 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:
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
Box 1: timestamp by - Box 2: TUMBLINGWINDOW - Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window. Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
HOTSPOT - You have an Azure Data Factory instance named ADF1 and two Azure Synapse Analytics workspaces named WS1 and WS2. ADF1 contains the following pipelines: ✑ P1: Uses a copy activity to copy data from a nonpartitioned table in a dedicated SQL pool of WS1 to an Azure Data Lake Storage Gen2 account ✑ P2: Uses a copy activity to copy data from text-delimited files in an Azure Data Lake Storage Gen2 account to a nonpartitioned table in a dedicated SQL pool of WS2 You need to configure P1 and P2 to maximize parallelism and performance. Which dataset settings should you configure for the copy activity if each pipeline? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area:
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
Box 1: Set the Copy method to PolyBase While SQL pool supports many loading methods including non-Polybase options such as BCP and SQL BulkCopy API, the fastest and most scalable way to load data is through PolyBase. PolyBase is a technology that accesses external data stored in Azure Blob storage or Azure Data Lake Store via the T-SQL language. Box 2: Set the Copy method to Bulk insert Polybase not possible for text files. Have to use Bulk insert. Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/load-data-overview
Source: Adapted from community discussion — marcin1212
HOTSPOT - You have an Azure Storage account that generates 200,000 new files daily. The file names have a format of {YYYY}/{MM}/{DD}/{HH}/{CustomerID}.csv. You need to design an Azure Data Factory solution that will load new data from the storage account to an Azure Data Lake once hourly. The solution must minimize load times and costs. How should you configure the solution? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Hot Area:
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
Box 1: Incremental load - Box 2: Tumbling window - 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. Reference: https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
Source: Adapted from community discussion — ItHYMeRIsh
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 Standard cluster for each data scientist, a Standard cluster for the data engineers, and a High Concurrency cluster for the jobs. Does this meet the goal?
Answer: B
B is correct but the explanation is wrong. ✑ A workload for data engineers who will use Python and SQL. --> high concurrency ✑ A workload for jobs that will run notebooks that use Python, Scala, and SQL. --> standard ✑ A workload that data scientists will use to perform ad hoc analysis in Scala and R. --> standard because high concurrency does not support Scala https://stackoverflow.com/questions/65869399/high-concurrency-clusters-in-databricks
Source: Adapted from community discussion — lukeonline
You have the following Azure Data Factory pipelines: ✑ Ingest Data from System1 ✑ Ingest Data from System2 ✑ Populate Dimensions ✑ Populate Facts Ingest Data from System1 and Ingest Data from System2 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System2. Populate Facts must execute after Populate Dimensions pipeline. All the pipelines must execute every eight hours. What should you do to schedule the pipelines for execution?
Answer: C
C is correct, but with poor wording. Should be 'parent pipeline' with a schedule trigger. The parent pipeline has 4 execute pipeline activities. Ingest 1 and Ingest 2 have no dependencies. Dimension pipeline has two dependencies from 'on completion' outputs of both Ingest 1 and Ingest 2 pipelines. Fact pipeline has one 'on completion' dependency on the Dimension pipeline. Absolutely nothing to do with a tumbling window trigger
Source: Adapted from community discussion — onyerleft
DRAG DROP - 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. Select and Place:
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
Step 1: an asymmetric key - A master key should be created only once in a database. The Database Master Key is a symmetric key used to protect the private keys of certificates and asymmetric keys in the database. Step 2: a database scoped credential Create a Database Scoped Credential. A Database Scoped Credential is a record that contains the authentication information required to connect an external resource. The master key needs to be created first before creating the database scoped credential. Step 3: an external data source - Create an External Data Source. External data sources are used to establish connectivity for data loading using Polybase. Reference: https://www.sqlservercentral.com/articles/access-external-data-from-azure-synapse-analytics-using-polybase
Source: Adapted from community discussion — engrbrain
You are monitoring an Azure Stream Analytics job by using metrics in Azure. You discover that during the last 12 hours, the average watermark delay is consistently greater than the configured late arrival tolerance. What is a possible cause of this behavior?
Answer: D
HOTSPOT - 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:
This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.
Answer: See figure below
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. Reference: 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
Source: Adapted from community discussion — adfgasd
Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page
DP-203 practice, question 191. 370 questions available.
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