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The DP-420 certification exam covers a range of topics related to the design and implementation of cloud-native applications using Azure Cosmos DB. These topics include data modeling, partitioning, indexing, querying, and managing data at scale. DP-420 Exam also covers topics related to the use of Azure Cosmos DB in cloud-native architectures, including the use of Azure Functions, Azure Event Grid, and Azure Service Bus.
NEW QUESTION # 168
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 a container named container1 in an Azure Cosmos DB Core (SQL) API account.
You need to make the contents of container1 available as reference data for an Azure Stream Analytics job.
Solution: You create an Azure Data Factory pipeline that uses Azure Cosmos DB Core (SQL) API as the input and Azure Blob Storage as the output.
Does this meet the goal?
Answer: B
Explanation:
Instead create an Azure function that uses Azure Cosmos DB Core (SQL) API change feed as a trigger and Azure event hub as the output.
The Azure Cosmos DB change feed is a mechanism to get a continuous and incremental feed of records from an Azure Cosmos container as those records are being created or modified. Change feed support works by listening to container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.
The following diagram represents the data flow and components involved in the solution:
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/sql/changefeed-ecommerce-solution
NEW QUESTION # 169
You have a container named container1 in an Azure Cosmos DB Core (SQL) API account. Upserts of items in container1 occur every three seconds.
You have an Azure Functions app named function1 that is supposed to run whenever items are inserted or replaced in container1.
You discover that function1 runs, but not on every upsert.
You need to ensure that function1 processes each upsert within one second of the upsert.
Which property should you change in the Function.json file of function1?
Answer: D
Explanation:
Explanation
With an upsert operation we can either insert or update an existing record at the same time.
FeedPollDelay: The time (in milliseconds) for the delay between polling a partition for new changes on the feed, after all current changes are drained. Default is 5,000 milliseconds, or 5 seconds.
Reference: https://docs.microsoft.com/en-us/azure/azure-functions/functions-bindings-cosmosdb-v2-trigger
NEW QUESTION # 170
You are developing a web app named App1 that will store customer-generated content in an Azure Cosmos DB for NoSQL database named DB1.
You configure an Azure Al Search indexer to automatically ingest documents from DB1 into an Azure Al Search index.
App1 uses REST APIs to provide advanced search and retrieval capabilities for the documents.
You need to identify which types of queries you can use in Azure Al Search to retrieve text data. The solution must minimize development effort Which two query types can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
NEW QUESTION # 171
Hotspot Question
You have an Apache Spark pool in Azure Synapse Analytics that runs the following Python code in a notebook.
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:
Box 1: No
Streaming Append Output Mode is an outputMode in which only the new rows in the streaming DataFrame/Dataset will be written to the sink.
This is the default mode. Use append as output mode outputMode("append") when you want to output only new rows to the output sink.
Note:
Streaming Complete Output Mode is an OutputMode in which all the rows in the streaming DataFrame/Dataset will be written to the sink every time there are some updates.
Streaming Update Output Mode is an outputMode in which only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates.
Box 2: No
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive.
Box 3: No
Reference:
https://sparkbyexamples.com/spark/spark-streaming-outputmode/
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html
https://docs.microsoft.com/en-us/azure/synapse-analytics/synapse-link/how-to-query-analytical-store-spark
NEW QUESTION # 172
You have an Azure Cosmos DB for NoSQL account that uses the default consistency level. How can the consistency level be modified as part of a query request?
Answer: A
NEW QUESTION # 173
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