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NEW QUESTION: 1
Your data integrator environment interprets year values greater than 15 as 1915 instead of 2015. you must ensure data integrator interprets any date from "00 to 90" as "2000 to 2090" without making direct modifications to the underlying data flow. Which method should you use to accomplish this task?
A. Configure the source database to interpret the two digit dates appropriately.
B. On the job server, open the windows l control panel l regional settings l Customize data and modify the two digit year interpretation to 90.
C. Open the web administration tool and select management l requisiteness edit the production requsitury and modify the "Century change year to 90".
D. Log into the designer and select tools l Options l data l General and modify the "Century change year" to 90.
E. Open the server manger and select edit job server config and modify the "Century change year to 90".
Answer: D

NEW QUESTION: 2
DRAG DROP

Answer:
Explanation:

NEW QUESTION: 3
You need to recommend the appropriate storage and processing solution?
What should you recommend?
A. Flush the blob cache using Windows PowerShell.
B. Enable Databricks IO (DBIO) caching.
C. Enable Apache Spark RDD (RDD) caching.
D. Configure the reading speed using Azure Data Studio.
E. Enable auto-shrink on the database.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Scenario: You must be able to use a file system view of data stored in a blob. You must build an
architecture that will allow Contoso to use the DB FS filesystem layer over a blob store.
Databricks File System (DBFS) is a distributed file system installed on Azure Databricks clusters. Files in
DBFS persist to Azure Blob storage, so you won't lose data even after you terminate a cluster.
The Databricks Delta cache, previously named Databricks IO (DBIO) caching, accelerates data reads by
creating copies of remote files in nodes' local storage using a fast intermediate data format. The data is
cached automatically whenever a file has to be fetched from a remote location. Successive reads of the
same data are then performed locally, which results in significantly improved reading speed.
References:
https://docs.databricks.com/delta/delta-cache.html#delta-cache
Question Set 4