[Oct 05, 2021] Step by Step Guide to Prepare for DP-200 Exam BrainDumps [Q35-Q57]

Oct 05, 2021 Step by Step Guide to Prepare for DP-200 Exam BrainDumps

Azure Data Engineer Associate DP-200 Real Exam Questions and Answers FREE Updated on 2021

NEW QUESTION 35
You need to set up access to Azure SQL Database for Tier 7 and Tier 8 partners.
Which three actions should you perform in sequence? To answer, move the appropriate three actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation

Tier 7 and 8 data access is constrained to single endpoints managed by partners for access Step 1: Set the Allow Azure Services to Access Server setting to Disabled Set Allow access to Azure services to OFF for the most secure configuration.
By default, access through the SQL Database firewall is enabled for all Azure services, under Allow access to Azure services. Choose OFF to disable access for all Azure services.
Note: The firewall pane has an ON/OFF button that is labeled Allow access to Azure services. The ON setting allows communications from all Azure IP addresses and all Azure subnets. These Azure IPs or subnets might not be owned by you. This ON setting is probably more open than you want your SQL Database to be. The virtual network rule feature offers much finer granular control.
Step 2: In the Azure portal, create a server firewall rule
Set up SQL Database server firewall rules
Server-level IP firewall rules apply to all databases within the same SQL Database server.
To set up a server-level firewall rule:
* In Azure portal, select SQL databases from the left-hand menu, and select your database on the SQL databases page.
* On the Overview page, select Set server firewall. The Firewall settings page for the database server opens.
Step 3: Connect to the database and use Transact-SQL to create a database firewall rule Database-level firewall rules can only be configured using Transact-SQL (T-SQL) statements, and only after you've configured a server-level firewall rule.
To setup a database-level firewall rule:
* Connect to the database, for example using SQL Server Management Studio.
* In Object Explorer, right-click the database and select New Query.
* In the query window, add this statement and modify the IP address to your public IP address:
* EXECUTE sp_set_database_firewall_rule N'Example DB Rule','0.0.0.4','0.0.0.4';
* On the toolbar, select Execute to create the firewall rule.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-security-tutorial

NEW QUESTION 36
You need to implement event processing by using Stream Analytics to produce consistent JSON documents.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Define a query that contains a JavaScript user-defined aggregates (UDA) function.
B. Define an output to Azure Data Lake Storage Gen2.
C. Define a transformation query.
D. Define a stream input.
E. Define an output to Cosmos DB.
F. Define a reference input.

Answer: B,C,D

Explanation:
* DOCDB stored documents that connect to the sales data in SALESDB. The documents are stored in two different JSON formats based on the sales channel.
* The sales data including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytic process will perform aggregations that must be done continuously, without gaps, and without overlapping.
* As they arrive, all the sales documents in JSON format must be transformed into one consistent format.
Manage and develop data processing
Question Set 1

NEW QUESTION 37
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 High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
No need for a High Concurrency cluster for each data scientist.
Standard clusters are recommended for a single user. Standard can run workloads developed in any language:
Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
References:
https://docs.azuredatabricks.net/clusters/configure.html

NEW QUESTION 38
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 questions 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 need to configure data encryption for external applications.
Solution:
1. Access the Always Encrypted Wizard in SQL Server Management Studio
2. Select the column to be encrypted
3. Set the encryption type to Deterministic
4. Configure the master key to use the Azure Key Vault
5. Validate configuration results and deploy the solution
Does the solution meet the goal?

A. No
B. Yes

Answer: B

Explanation:
Explanation
We use the Azure Key Vault, not the Windows Certificate Store, to store the master key.
Note: The Master Key Configuration page is where you set up your CMK (Column Master Key) and select the key store provider where the CMK will be stored. Currently, you can store a CMK in the Windows certificate store, Azure Key Vault, or a hardware security module (HSM).

References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-always-encrypted-azure-key-vault

NEW QUESTION 39
You need to process and query ingested Tier 9 data.
Which two options should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Transact-SQL statements
B. Azure Event Grid
C. Azure Notification Hub
D. Azure Cache for Redis
E. Azure Stream Analytics
F. Apache Kafka statements

Answer: B,E

Explanation:
Event Hubs provides a Kafka endpoint that can be used by your existing Kafka based applications as an alternative to running your own Kafka cluster.
You can stream data into Kafka-enabled Event Hubs and process it with Azure Stream Analytics, in the following steps:
* Create a Kafka enabled Event Hubs namespace.
* Create a Kafka client that sends messages to the event hub.
* Create a Stream Analytics job that copies data from the event hub into an Azure blob storage.
Scenario:

Tier 9 reporting must be moved to Event Hubs, queried, and persisted in the same Azure region as the company's main office References:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-kafka-stream-analytics

NEW QUESTION 40
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 questions 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 need to implement diagnostic logging for Data Warehouse monitoring.
Which log should you use?

A. SqlRequests
B. RequestSteps
C. ExecRequests
D. DmsWorkers

Answer: A

Explanation:
Explanation
Scenario:
The Azure SQL Data Warehouse cache must be monitored when the database is being used.

References:
https://docs.microsoft.com/en-us/sql/relational-databases/system-dynamic-management-views/sys-dm-pdw-sql-r

NEW QUESTION 41
A company builds an application to allow developers to share and compare code. The conversations, code snippets, and links shared by people in the application are stored in a Microsoft Azure SQL Database instance.
The application allows for searches of historical conversations and code snippets.
When users share code snippets, the code snippet is compared against previously share code snippets by using a combination of Transact-SQL functions including SUBSTRING, FIRST_VALUE, and SQRT. If a match is found, a link to the match is added to the conversation.
Customers report the following issues:
* Delays occur during live conversations
* A delay occurs before matching links appear after code snippets are added to conversations You need to resolve the performance issues.
Which technologies should you use? To answer, drag the appropriate technologies to the correct issues. 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.

Answer:

Explanation:

Explanation

Box 1: memory-optimized table
In-Memory OLTP can provide great performance benefits for transaction processing, data ingestion, and transient data scenarios.
Box 2: materialized view
To support efficient querying, a common solution is to generate, in advance, a view that materializes the data in a format suited to the required results set. The Materialized View pattern describes generating prepopulated views of data in environments where the source data isn't in a suitable format for querying, where generating a suitable query is difficult, or where query performance is poor due to the nature of the data or the data store.
These materialized views, which only contain data required by a query, allow applications to quickly obtain the information they need. In addition to joining tables or combining data entities, materialized views can include the current values of calculated columns or data items, the results of combining values or executing transformations on the data items, and values specified as part of the query. A materialized view can even be optimized for just a single query.
References:
https://docs.microsoft.com/en-us/azure/architecture/patterns/materialized-view

NEW QUESTION 42

Use the following login credentials as needed:
Azure Username: xxxxx
Azure Password: xxxxx
The following information is for technical support purposes only:
Lab Instance: 10277521
You plan to generate large amounts of real-time data that will be copied to Azure Blob storage.
You plan to create reports that will read the data from an Azure Cosmos DB database.
You need to create an Azure Stream Analytics job that will input the data from a blob storage named storage10277521 to the Cosmos DB database.
To complete this task, sign in to the Azure portal.

Answer:

Explanation:
See the explanation below.
Explanation
Step 1: Create a Stream Analytics job
1. Sign in to the Azure portal.
2. Select Create a resource in the upper left-hand corner of the Azure portal.
3. Select Analytics > Stream Analytics job from the results list.
4. Fill out the Stream Analytics job page.

5. Check the Pin to dashboard box to place your job on your dashboard and then select Create.
6. You should see a Deployment in progress... notification displayed in the top right of your browser window.
Step 2: Configure job input
1. Navigate to your Stream Analytics job.
2. Select Inputs > Add Stream input > Azure Blob storage

3. In the Azure Blob storage setting choose: storage10277521. Leave other options to default values and select Save to save the settings.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-quick-create-portal

NEW QUESTION 43
You manage the Microsoft Azure Databricks environment for a company. You must be able to access a private Azure Blob Storage account. Data must be available to all Azure Databricks workspaces. You need to provide the data access.
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: Create a secret scope
Step 2: Add secrets to the scope
Note: dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>") gets the key that has been stored as a secret in a secret scope.
Step 3: Mount the Azure Blob Storage container
You can mount a Blob Storage container or a folder inside a container through Databricks File System - DBFS. The mount is a pointer to a Blob Storage container, so the data is never synced locally.
Note: To mount a Blob Storage container or a folder inside a container, use the following command:
Python
dbutils.fs.mount(
source = "wasbs://<your-container-name>@<your-storage-account-name>.blob.core.windows.net", mount_point = "/mnt/<mount-name>", extra_configs = {"<conf-key>":dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>")}) where:
dbutils.secrets.get(scope = "<scope-name>", key = "<key-name>") gets the key that has been stored as a secret in a secret scope.
References:
https://docs.databricks.com/spark/latest/data-sources/azure/azure-storage.html

NEW QUESTION 44
You need to ensure polling data security requirements are met.
Which security technologies should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Azure Active Directory user
Scenario:
Access to polling data must set on a per-active directory user basis
Box 2: DataBase Scoped Credential
SQL Server uses a database scoped credential to access non-public Azure blob storage or Kerberos-secured Hadoop clusters with PolyBase.
PolyBase cannot authenticate by using Azure AD authentication.
References:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-database-scoped-credential-transact-sql

NEW QUESTION 45
You need to implement an Azure Databricks cluster that automatically connects to Azure Data Lake Storage Gen2 by using Azure Active Directory (Azure AD) integration.
How should you configure the new cluster? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:
Explanation

Box 1: High Concurrency
Enable Azure Data Lake Storage credential passthrough for a high-concurrency cluster.
Incorrect:
Support for Azure Data Lake Storage credential passthrough on standard clusters is in Public Preview.
Standard clusters with credential passthrough are supported on Databricks Runtime 5.5 and above and are limited to a single user.
Box 2: Azure Data Lake Storage Gen1 Credential Passthrough
You can authenticate automatically to Azure Data Lake Storage Gen1 and Azure Data Lake Storage Gen2 from Azure Databricks clusters using the same Azure Active Directory (Azure AD) identity that you use to log into Azure Databricks. When you enable your cluster for Azure Data Lake Storage credential passthrough, commands that you run on that cluster can read and write data in Azure Data Lake Storage without requiring you to configure service principal credentials for access to storage.
References:
https://docs.azuredatabricks.net/spark/latest/data-sources/azure/adls-passthrough.html

NEW QUESTION 46
Note: This question is a part of series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You develop a data ingestion process that will import data to an enterprise data warehouse in Azure Synapse Analytics. The data to be ingested resides in parquet files stored in an Azure Data Lake Gen 2 storage account.
You need to load the data from the Azure Data Lake Gen 2 storage account into the Data Warehouse.
Solution:
1. Create an external data source pointing to the Azure storage account
2. Create a workload group using the Azure storage account name as the pool name
3. Load the data using the CREATE TABLE AS SELECTstatement
Does the solution meet the goal?

A. No
B. Yes

Answer: A

Explanation:
Explanation/Reference:
Explanation:
Use the Azure Data Lake Gen 2 storage account.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake- store

NEW QUESTION 47
A company uses Microsoft Azure SQL Database to store sensitive company data. You encrypt the data and only allow access to specified users from specified locations.
You must monitor data usage, and data copied from the system to prevent data leakage.
You need to configure Azure SQL Database to email a specific user when data leakage occurs.
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:

NEW QUESTION 48

Use the following login credentials as needed:
Azure Username: xxxxx
Azure Password: xxxxx
The following information is for technical support purposes only:
Lab Instance: 10277521
You plan to query db3 to retrieve a list of sales customers. The query will retrieve several columns that include the email address of each sales customer.
You need to modify db3 to ensure that a portion of the email addresses is hidden in the query results.
To complete this task, sign in to the Azure portal.

Answer:

Explanation:
See the explanation below.
Explanation
1. Launch the Azure portal.
2. Navigate to the settings page of the database db3 that includes the sensitive data you want to mask.
3. Click the Dynamic Data Masking tile that launches the Dynamic Data Masking configuration page.
Note: Alternatively, you can scroll down to the Operations section and click Dynamic Data Masking.

4. In the Dynamic Data Masking configuration page, you may see some database columns that the recommendations engine has flagged for masking.

5. Click ADD MASK for the EmailAddress column
6. Click Save in the data masking rule page to update the set of masking rules in the dynamic data masking policy.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started-portal

NEW QUESTION 49
Which masking functions should you implement for each column to meet the data masking requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

NEW QUESTION 50
You deploy an Azure SQL database named DB1 to an Azure SQL server named SQL1.
Currently, only the server admin has access to DB1.
An Azure Active Directory (Azure AD) group named Analysts contains all the users who must have access to DB1.
You have the following data security requirements:
* The Analysts group must have read-only access to all the views and tables in the Sales schema of DB1.
* A manager will decide who can access DB1. The manager will not interact directly with DB1.
* Users must not have to manage a separate password solely to access DB1.
Which four actions should you perform in sequence to meet the data security requirements? 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: From the Azure Portal, set the Active Directory admin for SQL1.
Provision an Azure Active Directory administrator for your Azure SQL Database server.
You can provision an Azure Active Directory administrator for your Azure SQL server in the Azure portal and by using PowerShell.
Step 2: On DB1, create a contained user for the Analysts group by using Transact-SQL Create contained database users in your database mapped to Azure AD identities.
To create an Azure AD-based contained database user (other than the server administrator that owns the database), connect to the database with an Azure AD identity, as a user with at least the ALTER ANY USER permission. Then use the following Transact-SQL syntax:
CREATE USER <Azure_AD_principal_name> FROM EXTERNAL PROVIDER;
Step 3: From Microsoft SQL Server Management Studio (SSMS), sign in to SQL1 by using the account set as the Active Directory admin.
Connect to the user database or data warehouse by using SSMS or SSDT
To confirm the Azure AD administrator is properly set up, connect to the master database using the Azure AD administrator account. To provision an Azure AD-based contained database user (other than the server administrator that owns the database), connect to the database with an Azure AD identity that has access to the database.
Step 4: On DB1, grant the VIEW and SELECT DEFINTION..
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-aad-authentication-configure

NEW QUESTION 51
Your company uses several Azure HDInsight clusters.
The data engineering team reports several errors with some applications using these clusters.
You need to recommend a solution to review the health of the clusters.
What should you include in your recommendation?

A. Log Analytics
B. Azure Automation
C. Application Insights

Answer: A

Explanation:
Azure Monitor logs integration. Azure Monitor logs enables data generated by multiple resources such as HDInsight clusters, to be collected and aggregated in one place to achieve a unified monitoring experience.
As a prerequisite, you will need a Log Analytics Workspace to store the collected data. If you have not already created one, you can follow the instructions for creating a Log Analytics Workspace.
You can then easily configure an HDInsight cluster to send many workload-specific metrics to Log Analytics.
References:
https://azure.microsoft.com/sv-se/blog/monitoring-on-azure-hdinsight-part-2-cluster-health-and-availability/ Monitor and optimize data solutions Testlet 2 Background Proseware, Inc, develops and manages a product named Poll Taker. The product is used for delivering public opinion polling and analysis.
Polling data comes from a variety of sources, including online surveys, house-to-house interviews, and booths at public events.
Polling data
Polling data is stored in one of the two locations:
* An on-premises Microsoft SQL Server 2019 database named PollingData
* Azure Data Lake Gen 2
Data in Data Lake is queried by using PolyBase
Poll metadata
Each poll has associated metadata with information about the poll including the date and number of respondents. The data is stored as JSON.
Phone-based polling
Security
* Phone-based poll data must only be uploaded by authorized users from authorized devices
* Contractors must not have access to any polling data other than their own
* Access to polling data must set on a per-active directory user basis
Data migration and loading
* All data migration processes must use Azure Data Factory
* All data migrations must run automatically during non-business hours
* Data migrations must be reliable and retry when needed
Performance
After six months, raw polling data should be moved to a storage account. The storage must be available in the event of a regional disaster. The solution must minimize costs.
Deployments
* All deployments must be performed by using Azure DevOps. Deployments must use templates used in multiple environments
* No credentials or secrets should be used during deployments
Reliability
All services and processes must be resilient to a regional Azure outage.
Monitoring
All Azure services must be monitored by using Azure Monitor. On-premises SQL Server performance must be monitored.
Monitor and optimize data solutions
Testlet 3
Overview
Current environment
Contoso relies on an extensive partner network for marketing, sales, and distribution. Contoso uses external companies that manufacture everything from the actual pharmaceutical to the packaging.
The majority of the company's data reside in Microsoft SQL Server database. Application databases fall into one of the following tiers:

The company has a reporting infrastructure that ingests data from local databases and partner services.
Partners services consists of distributors, wholesales, and retailers across the world. The company performs daily, weekly, and monthly reporting.
Requirements
Tier 3 and Tier 6 through Tier 8 application must use database density on the same server and Elastic pools in a cost-effective manner.
Applications must still have access to data from both internal and external applications keeping the data encrypted and secure at rest and in transit.
A disaster recovery strategy must be implemented for Tier 3 and Tier 6 through 8 allowing for failover in the case of server going offline.
Selected internal applications must have the data hosted in single Microsoft Azure SQL Databases.
* Tier 1 internal applications on the premium P2 tier
* Tier 2 internal applications on the standard S4 tier
The solution must support migrating databases that support external and internal application to Azure SQL Database. The migrated databases will be supported by Azure Data Factory pipelines for the continued movement, migration and updating of data both in the cloud and from local core business systems and repositories.
Tier 7 and Tier 8 partner access must be restricted to the database only.
In addition to default Azure backup behavior, Tier 4 and 5 databases must be on a backup strategy that performs a transaction log backup eve hour, a differential backup of databases every day and a full back up every week.
Back up strategies must be put in place for all other standalone Azure SQL Databases using Azure SQL- provided backup storage and capabilities.
Databases
Contoso requires their data estate to be designed and implemented in the Azure Cloud. Moving to the cloud must not inhibit access to or availability of data.
Databases:
Tier 1 Database must implement data masking using the following masking logic:

Tier 2 databases must sync between branches and cloud databases and in the event of conflicts must be set up for conflicts to be won by on-premises databases.
Tier 3 and Tier 6 through Tier 8 applications must use database density on the same server and Elastic pools in a cost-effective manner.
Applications must still have access to data from both internal and external applications keeping the data encrypted and secure at rest and in transit.
A disaster recovery strategy must be implemented for Tier 3 and Tier 6 through 8 allowing for failover in the case of a server going offline.
Selected internal applications must have the data hosted in single Microsoft Azure SQL Databases.
* Tier 1 internal applications on the premium P2 tier
* Tier 2 internal applications on the standard S4 tier
Reporting
Security and monitoring
Security
A method of managing multiple databases in the cloud at the same time is must be implemented to streamlining data management and limiting management access to only those requiring access.
Monitoring
Monitoring must be set up on every database. Contoso and partners must receive performance reports as part of contractual agreements.
Tiers 6 through 8 must have unexpected resource storage usage immediately reported to data engineers.
The Azure SQL Data Warehouse cache must be monitored when the database is being used. A dashboard monitoring key performance indicators (KPIs) indicated by traffic lights must be created and displayed based on the following metrics:

Existing Data Protection and Security compliances require that all certificates and keys are internally managed in an on-premises storage.
You identify the following reporting requirements:
* Azure Data Warehouse must be used to gather and query data from multiple internal and external databases
* Azure Data Warehouse must be optimized to use data from a cache
* Reporting data aggregated for external partners must be stored in Azure Storage and be made available during regular business hours in the connecting regions
* Reporting strategies must be improved to real time or near real time reporting cadence to improve competitiveness and the general supply chain
* Tier 9 reporting must be moved to Event Hubs, queried, and persisted in the same Azure region as the company's main office
* Tier 10 reporting data must be stored in Azure Blobs
Issues
Team members identify the following issues:
* Both internal and external client application run complex joins, equality searches and group-by clauses.
Because some systems are managed externally, the queries will not be changed or optimized by Contoso
* External partner organization data formats, types and schemas are controlled by the partner companies
* Internal and external database development staff resources are primarily SQL developers familiar with the Transact-SQL language.
* Size and amount of data has led to applications and reporting solutions not performing are required speeds
* Tier 7 and 8 data access is constrained to single endpoints managed by partners for access
* The company maintains several legacy client applications. Data for these applications remains isolated form other applications. This has led to hundreds of databases being provisioned on a per application basis

NEW QUESTION 52
You have an ASP.NET web app that uses an Azure SQL database. The database contains a table named Employee. The table contains sensitive employee information, including a column named DateOfBirth.
You need to ensure that the data in the DateOfBirth column is encrypted both in the database and when transmitted between a client and Azure. Only authorized clients must be able to view the data in the column.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions in the answer area and arrange them in the correct order.

Answer:

Explanation:
Explanation

Reference:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-always-encrypted

NEW QUESTION 53
You configure monitoring for a Microsoft Azure SQL Data Warehouse implementation. The implementation uses PolyBase to load data from comma-separated value (CSV) files stored in Azure Data Lake Gen 2 using an external table.
Files with an invalid schema cause errors to occur.
You need to monitor for an invalid schema error.
For which error should you monitor?
EXTERNAL TABLE access failed due to internal error: 'Java exception raised on

A. for linked server "(null)", Query aborted- the maximum reject threshold (o rows) was reached while reading from an external source: 1 rows rejected out of total 1 rows processed.
EXTERNAL TABLE access failed due to internal error: 'Java exception raised on
B. call to HdfsBridge_Connect: Error [No FileSystem for scheme: wasbs] occurred while accessing external file.' Cannot execute the query "Remote Query" against OLE DB provider "SQLNCLI11":
C. call to HdfsBridge_Connect: Error
[com.microsoft.polybase.client.KerberosSecureLogin] occurred while accessing external file.' EXTERNAL TABLE access failed due to internal error: 'Java exception raised on
D. call to HdfsBridge_Connect: Error [Unable to instantiate LoginClass] occurred while accessing external file.'

Answer: A

Explanation:
Customer Scenario:
SQL Server 2016 or SQL DW connected to Azure blob storage. The CREATE EXTERNAL TABLE DDL points to a directory (and not a specific file) and the directory contains files with different schemas.
SSMS Error:
Select query on the external table gives the following error:
Msg 7320, Level 16, State 110, Line 14
Cannot execute the query "Remote Query" against OLE DB provider "SQLNCLI11" for linked server "(null)".
Query aborted-- the maximum reject threshold (0 rows) was reached while reading from an external source: 1 rows rejected out of total 1 rows processed.
Possible Reason:
The reason this error happens is because each file has different schema. The PolyBase external table DDL when pointed to a directory recursively reads all the files in that directory. When a column or data type mismatch happens, this error could be seen in SSMS.
Possible Solution:
If the data for each table consists of one file, then use the filename in the LOCATION section prepended by the directory of the external files. If there are multiple files per table, put each set of files into different directories in Azure Blob Storage and then you can point LOCATION to the directory instead of a particular file. The latter suggestion is the best practices recommended by SQLCAT even if you have one file per table.
Incorrect Answers:
A: Possible Reason: Kerberos is not enabled in Hadoop Cluster.
References:
https://techcommunity.microsoft.com/t5/DataCAT/PolyBase-Setup-Errors-and-Possible-Solutions/ba-p/305297

NEW QUESTION 54
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 scenario, 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 Sales in an Azure Cosmos DB database. Sales has 120 GB of data. Each entry in Sales has the following structure.

The partition key is set to the OrderId attribute.
Users report that when they perform queries that retrieve data by ProductName, the queries take longer than expected to complete.
You need to reduce the amount of time it takes to execute the problematic queries.
Solution: You create a lookup collection that uses ProductName as a partition key and OrderId as a value.
Does this meet the goal?

A. No
B. Yes

Answer: B

Explanation:
Explanation
One option is to have a lookup collection "ProductName" for the mapping of "ProductName" to "OrderId".
References:
https://azure.microsoft.com/sv-se/blog/azure-cosmos-db-partitioning-design-patterns-part-1/

NEW QUESTION 55
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 scenario, 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, Spark, 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 Databrick clusters for the workloads.
Solution: You create a Standard cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
We would need a High Concurrency cluster for the jobs.
Note:
Standard clusters are recommended for a single user. Standard can run workloads developed in any language: Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
References:
https://docs.azuredatabricks.net/clusters/configure.html

NEW QUESTION 56
Which masking functions should you implement for each column to meet the data masking requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Default
Default uses a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).
Only Show a zero value for the values in a column named ShockOilWeight.
Box 2: Credit Card
The Credit Card Masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card.
Example: XXXX-XXXX-XXXX-1234
Only show the last four digits of the values in a column named SuspensionSprings.
Scenario:
The company identifies the following data masking requirements for the Race Central data that will be stored in SQL Database:
Only Show a zero value for the values in a column named ShockOilWeight.
Only show the last four digits of the values in a column named SuspensionSprings.
Topic 4, ADatum Corporation
Case study
Overview
ADatum Corporation is a retailer that sells products through two sales channels: retail stores and a website.
Existing Environment
ADatum has one database server that has Microsoft SQL Server 2016 installed. The server hosts three mission-critical databases named SALESDB, DOCDB, and REPORTINGDB.
SALESDB collects data from the stored and the website.
DOCDB stored documents that connect to the sales data in SALESDB. The documents are stored in two different JSON formats based on the sales channel.
REPORTINGDB stores reporting data and contains server columnstore indexes. A daily process creates reporting data in REPORTINGDB from the data in SALESDB. The process is implemented as a SQL Server Integration Services (SSIS) package that runs a stored procedure from SALESDB.
Requirements
Planned Changes
ADatum plans to move the current data infrastructure to Azure. The new infrastructure has the following requirements:
* Migrate SALESDB and REPORTINGDB to an Azure SQL database.
* Migrate DOCDB to Azure Cosmos DB.
* The sales data including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytic process will perform aggregations that must be done continuously, without gaps, and without overlapping.
* As they arrive, all the sales documents in JSON format must be transformed into one consistent format.
* Azure Data Factory will replace the SSIS process of copying the data from SALESDB to REPORTINGDB.
Technical Requirements
The new Azure data infrastructure must meet the following technical requirements:
* Data in SALESDB must encrypted by using Transparent Data Encryption (TDE). The encryption must use your own key.
* SALESDB must be restorable to any given minute within the past three weeks.
* Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.
* Missing indexes must be created automatically for REPORTINGDB.
* Disk IO, CPU, and memory usage must be monitored for SALESDB.