DP-201 Free Update With 100% Exam Passing Guarantee [2021]
[Nov-2021] Verified Microsoft Exam Dumps with DP-201 Exam Study Guide
Exam DP-201: Designing an Azure Data Solution
Candidates for this exam are Microsoft Azure data engineers who collaborate with business stakeholders to identify and meet the data requirements to design data solutions that use Azure data services.
Azure data engineers are responsible for data-related design tasks that include designing Azure data storage solutions that use relational and non-relational data stores, batch and real-time data processing solutions, and data security and compliance solutions.
Candidates for this exam must design data solutions that use the following Azure services: Azure Cosmos DB, Azure Synapse Analytics, Azure Data Lake Storage, Azure Data Factory, Azure Stream Analytics, Azure Databricks, and Azure Blob storage.
Part of the requirements for: Microsoft Certified: Azure Data Engineer Associate
Preparation Materials and Resources
The Microsoft DP-201 exam is not easy and that is why you should take it with seriousness. If you want to pass the test at your first attempt, you need to devote enough time to preparation. It is recommended that you start studying for the exam with reviewing its objectives. Then you can proceed with the official preparation options available on the Microsoft webpage. The vendor offers the candidates two ways to prepare for the DP-201 test:
- Online learning: this training is available free of charge. There are several learning paths tackling various aspects of the Microsoft DP-201 exam.
- Instructor-led training: this is a paid course delivered under the guidance of the Microsoft authorized trainer. It lasts two days and is intended for the data professionals, data architects, as well as business intelligence professionals who want to enhance their expertise in data platform technologies available on the Microsoft Azure platform.
The students can use these training tools separately or in combination. Additionally, you can search for the relevant resources on other learning platforms.
NEW QUESTION 90
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 store delimited text files in an Azure Data Lake Storage account that will be organized into department folders.
You need to configure data access so that users see only the files in their respective department folder.
Solution: From the storage account, you enable a hierarchical namespace, and you use RBAC.
Does this meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
Disable the hierarchical namespace. And instead of RBAC use access control lists (ACLs).
Note: Azure Data Lake Storage implements an access control model that derives from HDFS, which in turn derives from the POSIX access control model.
Blob container ACLs does not support the hierarchical namespace, so it must be disabled.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-known-issues
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-access-control
NEW QUESTION 91
What should you recommend as a batch processing solution for Health Interface?
- A. Azure Stream Analytics
- B. Azure Data Factory
- C. Azure Databricks
- D. Azure CycleCloud
Answer: A
Explanation:
Scenario: ADatum identifies the following requirements for the Health Interface application:
Support a more scalable batch processing solution in Azure.
Reduce the amount of time it takes to add data from new hospitals to Health Interface.
Data Factory integrates with the Azure Cosmos DB bulk executor library to provide the best performance when you write to Azure Cosmos DB.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-cosmos-db Design data processing solutions Testlet 3 Case study This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview
Litware, Inc. owns and operates 300 convenience stores across the US. The company sells a variety of packaged foods and drinks, as well as a variety of prepared foods, such as sandwiches and pizzas.
Litware has a loyalty club whereby members can get daily discounts on specific items by providing their membership number at checkout.
Litware employs business analysts who prefer to analyze data by using Microsoft Power BI, and data scientists who prefer analyzing data in Azure Databricks notebooks.
Requirements. Business Goals
Litware wants to create a new analytics environment in Azure to meet the following requirements:
* See inventory levels across the stores. Data must be updated as close to real time as possible.
* Execute ad hoc analytical queries on historical data to identify whether the loyalty club discounts increase sales of the discounted products.
* Every four hours, notify store employees about how many prepared food items to produce based on historical demand from the sales data.
Requirements. Technical Requirements
Litware identifies the following technical requirements:
* Minimize the number of different Azure services needed to achieve the business goals.
* Use platform as a service (PaaS) offerings whenever possible and avoid having to provision virtual machines that must be managed by Litware.
* Ensure that the analytical data store is accessible only to the company's on-premises network and Azure services.
* Use Azure Active Directory (Azure AD) authentication whenever possible.
* Use the principle of least privilege when designing security.
* Stage inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store. Litware wants to remove transient data from Data Lake Storage once the data is no longer in use.
Files that have a modified date that is older than 14 days must be removed.
* Limit the business analysts' access to customer contact information, such as phone numbers, because this type of data is not analytically relevant.
* Ensure that you can quickly restore a copy of the analytical data store within one hour in the event of corruption or accidental deletion.
Requirements. Planned Environment
Litware plans to implement the following environment:
* The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure.
* Customer data, including name, contact information, and loyalty number, comes from Salesforce, a SaaS application, and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
* Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
* Daily inventory data comes from a Microsoft SQL server located on a private network.
* Litware currently has 5 TB of historical sales data and 100 GB of customer data. The company expects approximately 100 GB of new data per month for the next year.
* Litware will build a custom application named FoodPrep to provide store employees with the calculation results of how many prepared food items to produce every four hours.
* Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure.
NEW QUESTION 92
You are designing a real-time processing solution for maintenance work requests that are received via email.
The solution will perform the following actions:
* Store all email messages in an archive.
* Access weather forecast data by using the Python SDK for Azure Open Datasets.
* Identify high priority requests that will be affected by poor weather conditions and store the requests in an Azure SQL database.
The solution must minimize costs.
How should you complete the solution? To answer, drag the appropriate services to the correct locations. Each service 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.
Answer:
Explanation:
Explanation
Box 1: Azure Storage
Azure Event Hubs enables you to automatically capture the streaming data in Event Hubs in an Azure Blob storage or Azure Data Lake Storage Gen 1 or Gen 2 account of your choice, with the added flexibility of specifying a time or size interval. Setting up Capture is fast, there are no administrative costs to run it, and it scales automatically with Event Hubs throughput units. Event Hubs Capture is the easiest way to load streaming data into Azure, and enables you to focus on data processing rather than on data capture.
Box 2: Azure Logic Apps
You can monitor and manage events sent to Azure Event Hubs from inside a logic app with the Azure Event Hubs connector. That way, you can create logic apps that automate tasks and workflows for checking, sending, and receiving events from your Event Hub.
Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-capture-overview
https://docs.microsoft.com/en-us/azure/connectors/connectors-create-api-azure-event-hubs
NEW QUESTION 93
What should you recommend to prevent users outside the Litware on-premises network from accessing the analytical data store?
- A. a server-level firewall IP rule
- B. a server-level virtual network rule
- C. a database-level firewall IP rule
- D. a database-level virtual network rule
Answer: B
Explanation:
Virtual network rules are one firewall security feature that controls whether the database server for your single databases and elastic pool in Azure SQL Database or for your databases in SQL Data Warehouse accepts communications that are sent from particular subnets in virtual networks.
Server-level, not database-level: Each virtual network rule applies to your whole Azure SQL Database server, not just to one particular database on the server. In other words, virtual network rule applies at the serverlevel, not at the database-level.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-vnet-service-endpoint-rule-overview
NEW QUESTION 94
A company stores large datasets in Azure, including sales transactions and customer account information.
You must design a solution to analyze the data. You plan to create the following HDInsight clusters:
You need to ensure that the clusters support the query requirements.
Which cluster types should you recommend? To answer, select the appropriate configuration in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Interactive Query
Choose Interactive Query cluster type to optimize for ad hoc, interactive queries.
Box 2: Hadoop
Choose Apache Hadoop cluster type to optimize for Hive queries used as a batch process.
Note: In Azure HDInsight, there are several cluster types and technologies that can run Apache Hive queries.
When you create your HDInsight cluster, choose the appropriate cluster type to help optimize performance for your workload needs.
For example, choose Interactive Query cluster type to optimize for ad hoc, interactive queries. Choose Apache Hadoop cluster type to optimize for Hive queries used as a batch process. Spark and HBase cluster types can also run Hive queries.
References:
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/hdinsight-hadoop-optimize-hive-query?toc=%2Fko-kr%2F
NEW QUESTION 95
Which Azure data storage solution should you recommend for each application? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Health Review: Azure SQL Database
Scenario: ADatum identifies the following requirements for the Health Review application:
* Ensure that sensitive health data is encrypted at rest and in transit.
* Tag all the sensitive health data in Health Review. The data will be used for auditing.
Health Interface: Azure Cosmos DB
A Datum identifies the following requirements for the Health Interface application:
* Upgrade to a data storage solution that will provide flexible schemas and increased throughput for writing data. Data must be regionally located close to each hospital, and reads must display be the most recent committed version of an item.
* Reduce the amount of time it takes to add data from new hospitals to Health Interface.
* Support a more scalable batch processing solution in Azure.
* Reduce the amount of development effort to rewrite existing SQL queries.
Health Insights: Azure SQL Data Warehouse
Azure SQL Data Warehouse is a cloud-based enterprise data warehouse that leverages massively parallel processing (MPP) to quickly run complex queries across petabytes of data. Use SQL Data Warehouse as a key component of a big data solution.
You can access Azure SQL Data Warehouse (SQL DW) from Databricks using the SQL Data Warehouse connector (referred to as the SQL DW connector), a data source implementation for Apache Spark that uses Azure Blob Storage, and PolyBase in SQL DW to transfer large volumes of data efficiently between a Databricks cluster and a SQL DW instance.
Scenario: ADatum identifies the following requirements for the Health Insights application:
* The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables References:
https://docs.databricks.com/data/data-sources/azure/sql-data-warehouse.html
NEW QUESTION 96
You are designing an Azure Cosmos DB database that will support vertices and edges.
Which Cosmos DB API should you include in the design?
- A. Gremlin
- B. SQL
- C. Table
- D. Cassandra
Answer: A
Explanation:
Explanation
The Azure Cosmos DB Gremlin API can be used to store massive graphs with billions of vertices and edges.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/graph-introduction
NEW QUESTION 97
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.
Answer:
Explanation:
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 98
A company plans to use Azure SQL Database to support a line of business applications. The application will manage sensitive employee data.
The solution must meet the following requirements:
* Encryption must be performed by the application.
* Only the client application must have access keys for encrypting and decrypting data.
* Data must never appear as plain text in the database.
* The strongest possible encryption method must be used.
* Searching must be possible on selected data.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Always Encrypted with deterministic encryption
Deterministic encryption always generates the same encrypted value for any given plain text value. Using deterministic encryption allows point lookups, equality joins, grouping and indexing on encrypted columns.
However, it may also allow unauthorized users to guess information about encrypted values by examining patterns in the encrypted column, especially if there is a small set of possible encrypted values, such as True/False, or North/South/East/West region. Deterministic encryption must use a column collation with a binary2 sort order for character columns.
Box 2: Always Encrypted with Randomized encryption
* Randomized encryption uses a method that encrypts data in a less predictable manner. Randomized encryption is more secure, but prevents searching, grouping, indexing, and joining on encrypted columns.
Note: With Always Encrypted the Database Engine never operates on plaintext data stored in encrypted columns, but it still supports some queries on encrypted data, depending on the encryption type for the column. Always Encrypted supports two types of encryption: randomized encryption and deterministic encryption.
Use deterministic encryption for columns that will be used as search or grouping parameters, for example a government ID number. Use randomized encryption, for data such as confidential investigation comments, which are not grouped with other records and are not used to join tables.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/always-encrypted-database-engine
NEW QUESTION 99
You need to recommend a solution for storing customer data. What should you recommend?
- A. Azure SQL Data Warehouse
- B. Azure Databricks
- C. Azure Stream Analytics
- D. Azure SQL Database
Answer: B
Explanation:
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-az
Topic 1, Case study 1The company identifies the following business
requirements:
* External vendors must be able to perform custom analysis of data using machine learning technologies.
* You must display a dashboard on the operations status page that displays the following metrics: telemetry, volume, and processing latency.
* Traffic data must be made available to the Government Planning Department for the purpose of modeling changes to the highway system. The traffic data will be used in conjunction with other data such as information about events such as sporting events, weather conditions, and population statistics. External data used during the modeling is stored in on-premises SQL Server 2016 databases and CSV files stored in an Azure Data Lake Storage Gen2 storage account.
* Information about vehicles that have been detected as going over the speed limit during the last 30 minutes must be available to law enforcement officers. Several law enforcement organizations may respond to speeding vehicles.
* The solution must allow for searches of vehicle images by license plate to support law enforcement investigations. Searches must be able to be performed using a query language and must support fuzzy searches to compensate for license plate detection errors.
Telemetry Capture
The telemetry capture system records each time a vehicle passes in front of a sensor. The sensors run on a custom embedded operating system and record the following telemetry data:
* Time
* Location in latitude and longitude
* Speed in kilometers per hour (kmph)
* Length of vehicle in meters
Visual Monitoring
The visual monitoring system is a network of approximately 1,000 cameras placed near highways that capture images of vehicle traffic every 2 seconds. The cameras record high resolution images. Each image is approximately 3 MB in size.
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other question on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next sections of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question on this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the button to return to the question.
Requirements
Business
The company identifies the following business requirements:
* You must transfer all images and customer data to cloud storage and remove on-premises servers.
* You must develop an analytical processing solution for transforming customer data.
* You must develop an image object and color tagging solution.
* Capital expenditures must be minimized.
* Cloud resource costs must be minimized.
Technical
The solution has the following technical requirements:
* 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.
* Image data must be stored in a single data store at minimum cost.
* Customer data must be analyzed using managed Spark clusters.
* Power BI must be used to visualize transformed customer data.
* All data must be backed up in case disaster recovery is required.
Security and optimization
All cloud data must be encrypted at rest and in transit. The solution must support:
* parallel processing of customer data
* hyper-scale storage of images
* global region data replication of processed image data
NEW QUESTION 100
You are designing a solution for a company. You plan to use Azure Databricks.
You need to recommend workloads and tiers to meet the following requirements:
* Provide managed clusters for running production jobs.
* Provide persistent clusters that support auto-scaling for analytics processes.
* Provide role-based access control (RBAC) support for Notebooks.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Data Engineering Only
Box 2: Data Engineering and Data Analytics
Box 3: Standard
Box 4: Data Analytics only
Box 5: Premium
Premium required for RBAC. Data Analytics Premium Tier provide interactive workloads to analyze data collaboratively with notebooks References:
https://azure.microsoft.com/en-us/pricing/details/databricks/
NEW QUESTION 101
You plan to ingest streaming social media data by using Azure Stream Analytics. The data will be stored in files in Azure Data Lake Storage, and then consumed by using Azure Databricks and PolyBase in Azure Synapse Analytics.
You need to recommend a Stream Analytics data output format to ensure that the queries from Databricks and PolyBase against the files encounter the fewest possible errors. The solution must ensure that the files can be queried quickly and that the data type information is retained.
What should you recommend?
- A. JSON
- B. CSV
- C. Avro
- D. Parquet
Answer: C
Explanation:
Explanation/Reference:
Explanation:
The Avro format is great for data and message preservation.
Avro schema with its support for evolution is essential for making the data robust for streaming architectures like Kafka, and with the metadata that schema provides, you can reason on the data. Having a schema provides robustness in providing meta-data about the data stored in Avro records which are self-documenting the data.
References:
http://cloudurable.com/blog/avro/index.html
NEW QUESTION 102
You need to design network access to the SQL Server data.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
References:
https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/configure-a-server-to-listen-on-a-specific-tcp-port?view=sql-server-2017
NEW QUESTION 103
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 smaller than 250MB.
Does the solution meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
Ensure that files stored are larger, not smaller than 250MB.
You can have a separate compaction job that combines these files into larger ones.
Note: The file 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 104
Your company is an online retailer that can have more than 100 million orders during a 24-hour period, 95 percent of which are placed between 16:30 and 17:00. All the orders are in US dollars. The current product line contains the following three item categories:
* Games with 15,123 items
* Books with 35,312 items
* Pens with 6,234 items
You are designing an Azure Cosmos DB data solution for a collection named Orders Collection. The following documents is a typical order in Orders Collection.
Order Collection is expected to have a balanced read/write-intensive workload.
Which partition key provides the most efficient throughput?
- A. Item/Currency
- B. Item/Category
- C. OrderTime
- D. Item/id
Answer: B
Explanation:
Choose a partition key that has a wide range of values and access patterns that are evenly spread across logical partitions. This helps spread the data and the activity in your container across the set of logical partitions, so that resources for data storage and throughput can be distributed across the logical partitions.
Choose a partition key that spreads the workload evenly across all partitions and evenly over time. Your choice of partition key should balance the need for efficient partition queries and transactions against the goal of distributing items across multiple partitions to achieve scalability.
Candidates for partition keys might include properties that appear frequently as a filter in your queries. Queries can be efficiently routed by including the partition key in the filter predicate.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/partitioning-overview#choose-partitionkey
NEW QUESTION 105
What should you do to improve high availability of the real-time data processing solution?
- A. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.
- B. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
- C. Deploy a High Concurrency Databricks cluster.
- D. Set Data Lake Storage to use geo-redundant storage (GRS).
Answer: B
Explanation:
Guarantee Stream Analytics job reliability during service updates
Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure's paired region model.
Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure
NEW QUESTION 106
Your company is an online retailer that can have more than 100 million orders during a 24-hour period, 95 percent of which are placed between 16:30 and 17:00. All the orders are in US dollars. The current product line contains the following three item categories:
* Games with 15,123 items
* Books with 35,312 items
* Pens with 6,234 items
You are designing an Azure Cosmos DB data solution for a collection named Orders Collection. The following documents is a typical order in Orders Collection.
Order Collection is expected to have a balanced read/write-intensive workload.
Which partition key provides the most efficient throughput?
- A. Item/Currency
- B. Item/Category
- C. OrderTime
- D. Item/id
Answer: B
Explanation:
Choose a partition key that has a wide range of values and access patterns that are evenly spread across logical partitions. This helps spread the data and the activity in your container across the set of logical partitions, so that resources for data storage and throughput can be distributed across the logical partitions.
Choose a partition key that spreads the workload evenly across all partitions and evenly over time. Your choice of partition key should balance the need for efficient partition queries and transactions against the goal of distributing items across multiple partitions to achieve scalability.
Candidates for partition keys might include properties that appear frequently as a filter in your queries. Queries can be efficiently routed by including the partition key in the filter predicate.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/partitioning-overview#choose-partitionkey
NEW QUESTION 107
You discover that the highest chance of corruption or bad data occurs during nightly inventory loads.
You need to ensure that you can quickly restore the data to its state before the nightly load and avoid missing any streaming data.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Step 1: Before the nightly load, create a user-defined restore point
SQL Data Warehouse performs a geo-backup once per day to a paired data center. The RPO for a geo-restore is 24 hours. If you require a shorter RPO for geo-backups, you can create a user-defined restore point and restore from the newly created restore point to a new data warehouse in a different region.
Step 2: Restore the data warehouse to a new name on the same server.
Step 3: Swap the restored database warehouse name.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/backup-and-restore
NEW QUESTION 108
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