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NEW QUESTION 1

You need to design a sharding strategy for the Planning Assistance database. What should you recommend?

  • A. a list mapping shard map on the binary representation of the License Plate column
  • B. a range mapping shard map on the binary representation of the speed column
  • C. a list mapping shard map on the location column
  • D. a range mapping shard map on the time column

Answer: A

Explanation:
Data used for Planning Assistance must be stored in a sharded Azure SQL Database.
A shard typically contains items that fall within a specified range determined by one or more attributes of the data. These attributes form the shard key (sometimes referred to as the partition key). The shard key should be static. It shouldn't be based on data that might change.
References:
https://docs.microsoft.com/en-us/azure/architecture/patterns/sharding

NEW QUESTION 2

You are evaluating data storage solutions to support a new application.
You need to recommend a data storage solution that represents data by using nodes and relationships in graph structures.
Which data storage solution should you recommend?

  • A. Blob Storage
  • B. Cosmos DB
  • C. Data Lake Store
  • D. HDInsight

Answer: B

Explanation:
For large graphs with lots of entities and relationships, you can perform very complex analyses very quickly. Many graph databases provide a query language that you can use to traverse a network of relationships efficiently.
Relevant Azure service: Cosmos DB
References:
https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-overview

NEW QUESTION 3

You are designing a recovery strategy for your Azure SQL Databases.
The recovery strategy must use default automated backup settings. The solution must include a Point-in time restore recovery strategy.
You need to recommend which backups to use and the order in which to restore backups.
What should you recommend? To answer, select the appropriate configuration in the answer area.
NOTE: Each correct selection is worth one point.
DP-201 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
All Basic, Standard, and Premium databases are protected by automatic backups. Full backups are taken every week, differential backups every day, and log backups every 5 minutes.
References:
https://azure.microsoft.com/sv-se/blog/azure-sql-database-point-in-time-restore/

NEW QUESTION 4

A company is evaluating data storage solutions.
You need to recommend a data storage solution that meets the following requirements: Minimize costs for storing blob objects.
Optimize access for data that is infrequently accessed. Data must be stored for at least 30 days.
Data availability must be at least 99 percent. What should you recommend?

  • A. Premium
  • B. Cold
  • C. Hot
  • D. Archive

Answer: B

Explanation:
Azure’s cool storage tier, also known as Azure cool Blob storage, is for infrequently-accessed data that needs to be stored for a minimum of 30 days. Typical use cases include backing up data before tiering to archival systems, legal data, media files, system audit information, datasets used for big data analysis and more.
The storage cost for this Azure cold storage tier is lower than that of hot storage tier. Since it is expected that the data stored in this tier will be accessed less frequently, the data access charges are high when compared to hot tier. There are no additional changes required in your applications as these tiers can be accessed using
APIs in the same manner that you access Azure storage. References:
https://cloud.netapp.com/blog/low-cost-storage-options-on-azure

NEW QUESTION 5

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.
A company is developing a solution to manage inventory data for a group of automotive repair shops. The
solution will use Azure SQL Data Warehouse as the data store. Shops will upload data every 10 days.
Data corruption checks must run each time data is uploaded. If corruption is detected, the corrupted data must be removed.
You need to ensure that upload processes and data corruption checks do not impact reporting and analytics processes that use the data warehouse.
Proposed solution: Insert data from shops and perform the data corruption check in a transaction. Rollback transfer if corruption is detected.
Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
Instead, create a user-defined restore point before data is uploaded. Delete the restore point after data corruption checks complete.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/backup-and-restore

NEW QUESTION 6

A company has an application that uses Azure SQL Database as the data store.
The application experiences a large increase in activity during the last month of each year.
You need to manually scale the Azure SQL Database instance to account for the increase in data write operations.
Which scaling method should you recommend?

  • A. Scale up by using elastic pools to distribute resources.
  • B. Scale out by sharding the data across databases.
  • C. Scale up by increasing the database throughput units.

Answer: C

Explanation:
As of now, the cost of running an Azure SQL database instance is based on the number of Database Throughput Units (DTUs) allocated for the database. When determining the number of units to allocate for the
solution, a major contributing factor is to identify what processing power is needed to handle the volume of expected requests.
Running the statement to upgrade/downgrade your database takes a matter of seconds.

NEW QUESTION 7

You design data engineering solutions for a company.
You must integrate on-premises SQL Server data into an Azure solution that performs Extract-Transform-Load (ETL) operations have the following requirements:
DP-201 dumps exhibit Develop a pipeline that can integrate data and run notebooks.
DP-201 dumps exhibit Develop notebooks to transform the data.
DP-201 dumps exhibit Load the data into a massively parallel processing database for later analysis. You need to recommend a solution.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
DP-201 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
DP-201 dumps exhibit

NEW QUESTION 8

You are designing an application. You plan to use Azure SQL Database to support the application.
The application will extract data from the Azure SQL Database and create text documents. The text documents will be placed into a cloud-based storage solution. The text storage solution must be accessible from an SMB network share.
You need to recommend a data storage solution for the text documents. Which Azure data storage type should you recommend?

  • A. Queue
  • B. Files
  • C. Blob
  • D. Table

Answer: B

Explanation:
Azure Files enables you to set up highly available network file shares that can be accessed by using the standard Server Message Block (SMB) protocol.
References:
https://docs.microsoft.com/en-us/azure/storage/common/storage-introduction https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview

NEW QUESTION 9

You are designing a Spark job that performs batch processing of daily web log traffic.
When you deploy the job in the production environment, it must meet the following requirements:
DP-201 dumps exhibit Run once a day.
DP-201 dumps exhibit Display status information on the company intranet as the job runs. You need to recommend technologies for triggering and monitoring jobs.
Which technologies should you recommend? To answer, drag the appropriate technologies to the correct locations. Each technology 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.
DP-201 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: Livy
You can use Livy to run interactive Spark shells or submit batch jobs to be run on Spark. Box 2: Beeline
Apache Beeline can be used to run Apache Hive queries on HDInsight. You can use Beeline with Apache Spark.
Note: Beeline is a Hive client that is included on the head nodes of your HDInsight cluster. Beeline uses JDBC to connect to HiveServer2, a service hosted on your HDInsight cluster. You can also use Beeline to access Hive on HDInsight remotely over the internet.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-livy-rest-interface https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-use-hive-beeline

NEW QUESTION 10

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 are designing an HDInsight/Hadoop cluster solution that uses Azure Data Lake Gen1 Storage. The solution requires POSIX permissions and enables diagnostics logging for auditing.
You need to recommend solutions that optimize storage.
Proposed Solution: Ensure that files stored are larger than 250MB. Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: A

Explanation:
Depending on what services and workloads are using the data, a good size to consider for files is 256 MB or greater. If the file sizes cannot be batched when landing in Data Lake Storage Gen1, you can have a separate compaction job that combines these files into larger ones.
Note: POSIX permissions and auditing in Data Lake Storage Gen1 comes with an overhead that becomes apparent when working with numerous small files. As a best practice, you must batch your data into larger files versus writing thousands or millions of small files to Data Lake Storage Gen1. Avoiding small file sizes can have multiple benefits, such as:
Lowering the authentication checks across multiple files Reduced open file connections
Faster copying/replication
Fewer files to process when updating Data Lake Storage Gen1 POSIX permissions References:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-best-practices

NEW QUESTION 11

A company installs IoT devices to monitor its fleet of delivery vehicles. Data from devices is collected from Azure Event Hub.
The data must be transmitted to Power BI for real-time data visualizations. You need to recommend a solution.
What should you recommend?

  • A. Azure HDInsight with Spark Streaming
  • B. Apache Spark in Azure Databricks
  • C. Azure Stream Analytics
  • D. Azure HDInsight with Storm

Answer: C

Explanation:
Step 1: Get your IoT hub ready for data access by adding a consumer group.
Step 2: Create, configure, and run a Stream Analytics job for data transfer from your IoT hub to your Power BI account.
Step 3: Create and publish a Power BI report to visualize the data. References:
https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-live-data-visualization-in-power-bi

NEW QUESTION 12

You design data engineering solutions for a company.
A project requires analytics and visualization of large set of data. The project has the following requirements:
DP-201 dumps exhibit Notebook scheduling
DP-201 dumps exhibit Cluster automation
DP-201 dumps exhibit Power BI Visualization
You need to recommend the appropriate Azure service. Which Azure service should you recommend?

  • A. Azure Batch
  • B. Azure Stream Analytics
  • C. Azure ML Studio
  • D. Azure Databricks
  • E. Azure HDInsight

Answer: D

Explanation:
A databrick job is a way of running a notebook or JAR either immediately or on a scheduled basis.
Azure Databricks has two types of clusters: interactive and job. Interactive clusters are used to analyze data collaboratively with interactive notebooks. Job clusters are used to run fast and robust automated workloads using the UI or API.
You can visualize Data with Azure Databricks and Power BI Desktop.
References:
https://docs.azuredatabricks.net/user-guide/clusters/index.html https://docs.azuredatabricks.net/user-guide/jobs.html

NEW QUESTION 13

You need to recommend the appropriate storage and processing solution? What should you recommend?

  • A. Enable auto-shrink on the database.
  • B. Flush the blob cache using Windows PowerShell.
  • C. Enable Apache Spark RDD (RDD) caching.
  • D. Enable Databricks IO (DBIO) caching.
  • E. Configure the reading speed using Azure Data Studio.

Answer: C

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.

NEW QUESTION 14

You are designing a data processing solution that will run as a Spark job on an HDInsight cluster. The solution will be used to provide near real-time information about online ordering for a retailer.
The solution must include a page on the company intranet that displays summary information. The summary information page must meet the following requirements:
DP-201 dumps exhibit Display a summary of sales to date grouped by product categories, price range, and review scope.
DP-201 dumps exhibit Display sales summary information including total sales, sales as compared to one day ago and sales as compared to one year ago.
DP-201 dumps exhibit Reflect information for new orders as quickly as possible. You need to recommend a design for the solution.
What should you recommend? To answer, select the appropriate configuration in the answer area.
DP-201 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: DataFrame
DataFrames
Best choice in most situations.
Provides query optimization through Catalyst. Whole-stage code generation.
Direct memory access.
Low garbage collection (GC) overhead.
Not as developer-friendly as DataSets, as there are no compile-time checks or domain object programming. Box 2: parquet
The best format for performance is parquet with snappy compression, which is the default in Spark 2.x. Parquet stores data in columnar format, and is highly optimized in Spark.

NEW QUESTION 15

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 are designing an Azure SQL Database that will use elastic pools. You plan to store data about customers in a table. Each record uses a value for CustomerID.
You need to recommend a strategy to partition data based on values in CustomerID. Proposed Solution: Separate data into customer regions by using vertical partitioning. Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
Vertical partitioning is used for cross-database queries. Instead we should use Horizontal Partitioning, which also is called charding.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview

NEW QUESTION 16

You plan to use an Azure SQL data warehouse to store the customer data. You need to recommend a disaster recovery solution for the data warehouse. What should you include in the recommendation?

  • A. AzCopy
  • B. Read-only replicas
  • C. AdICopy
  • D. Geo-Redundant backups

Answer: D

Explanation:
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/backup-and-restore

NEW QUESTION 17

You are designing an Azure SQL Data Warehouse. You plan to load millions of rows of data into the data warehouse each day.
You must ensure that staging tables are optimized for data loading. You need to design the staging tables.
What type of tables should you recommend?

  • A. Round-robin distributed table
  • B. Hash-distributed table
  • C. Replicated table
  • D. External table

Answer: A

Explanation:
To achieve the fastest loading speed for moving data into a data warehouse table, load data into a staging table. Define the staging table as a heap and use round-robin for the distribution option.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

NEW QUESTION 18

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 are designing an Azure SQL Database that will use elastic pools. You plan to store data about customers in a table. Each record uses a value for CustomerID.
You need to recommend a strategy to partition data based on values in CustomerID. Proposed Solution: Separate data into customer regions by using horizontal partitioning. Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
We should use Horizontal Partitioning through Sharding, not divide through regions.
Note: Horizontal Partitioning - Sharding: Data is partitioned horizontally to distribute rows across a scaled out data tier. With this approach, the schema is identical on all participating databases. This approach is also called “sharding”. Sharding can be performed and managed using (1) the elastic database tools libraries or (2)
self-sharding. An elastic query is used to query or compile reports across many shards.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-query-overview

NEW QUESTION 19

You need to design the image processing solution to meet the optimization requirements for image tag data. What should you configure? To answer, drag the appropriate setting to the correct drop targets.
Each source 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.
DP-201 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Tagging data must be uploaded to the cloud from the New York office location.
Tagging data must be replicated to regions that are geographically close to company office locations.

NEW QUESTION 20

A company has many applications. Each application is supported by separate on-premises databases. You must migrate the databases to Azure SQL Database. You have the following requirements: Organize databases into groups based on database usage.
Define the maximum resource limit available for each group of databases.
You need to recommend technologies to scale the databases to support expected increases in demand. What should you recommend?

  • A. Read scale-out
  • B. Managed instances
  • C. Elastic pools
  • D. Database sharding

Answer: C

Explanation:
SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single Azure SQL Database server and share a set number of resources at a set price.
You can configure resources for the pool based either on the DTU-based purchasing model or the vCorebased purchasing model.

NEW QUESTION 21

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.
A company is developing a solution to manage inventory data for a group of automotive repair shops. The solution will use Azure SQL Data Warehouse as the data store.
Shops will upload data every 10 days.
Data corruption checks must run each time data is uploaded. If corruption is detected, the corrupted data must be removed.
You need to ensure that upload processes and data corruption checks do not impact reporting and analytics processes that use the data warehouse.
Proposed solution: Configure database-level auditing in Azure SQL Data Warehouse and set retention to 10 days.
Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
Instead, create a user-defined restore point before data is uploaded. Delete the restore point after data corruption checks complete.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/backup-and-restore

NEW QUESTION 22

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 are designing an HDInsight/Hadoop cluster solution that uses Azure Data Lake Gen1 Storage. The solution requires POSIX permissions and enables diagnostics logging for auditing.
You need to recommend solutions that optimize storage.
Proposed Solution: Implement compaction jobs to combine small files into larger files. Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: A

Explanation:
Depending on what services and workloads are using the data, a good size to consider for files is 256 MB or greater. If the file sizes cannot be batched when landing in Data Lake Storage Gen1, you can have a separate compaction job that combines these files into larger ones.
Note: POSIX permissions and auditing in Data Lake Storage Gen1 comes with an overhead that becomes apparent when working with numerous small files. As a best practice, you must batch your data into larger files versus writing thousands or millions of small files to Data Lake Storage Gen1. Avoiding small file sizes can have multiple benefits, such as:
Lowering the authentication checks across multiple files Reduced open file connections
Faster copying/replication
Fewer files to process when updating Data Lake Storage Gen1 POSIX permissions References:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-best-practices

NEW QUESTION 23

You need to recommend a solution for storing customer data. What should you recommend?

  • A. Azure SQL Data Warehouse
  • B. Azure Stream Analytics
  • C. Azure Databricks
  • D. Azure SQL Database

Answer: C

Explanation:
From the scenario:
Customer data must be analyzed using managed Spark clusters.
All cloud data must be encrypted at rest and in transit. The solution must support: parallel processing of customer data.
References:
https://www.microsoft.com/developerblog/2019/01/18/running-parallel-apache-spark-notebook-workloads-on-a

NEW QUESTION 24
......

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