:Microsoft Certified: Azure Data Engineer Associate
Vendor
:Microsoft
Total Questions
:380 Q&As
Last Updated
:May 16, 2024
Microsoft Microsoft Certified: Azure Data Engineer Associate DP-203 Questions & Answers
Question 11:
DRAG DROP
You need to ensure that the Twitter feed data can be analyzed in the dedicated SQL pool. The solution must meet the customer sentiment analytic requirements.
Which three Transact-SQL DDL commands should you run in sequence? To answer, move the appropriate commands from the list of commands to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Select and Place:
Correct Answer:
Scenario: Allow Contoso users to use PolyBase in an Azure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must
be authenticated by using their own Azure AD credentials.
Box 1: CREATE EXTERNAL DATA SOURCE
External data sources are used to connect to storage accounts.
Box 2: CREATE EXTERNAL FILE FORMAT
CREATE EXTERNAL FILE FORMAT creates an external file format object that defines external data stored in Azure Blob Storage or Azure Data Lake Storage. Creating an external file format is a prerequisite for creating an external table.
Box 3: CREATE EXTERNAL TABLE AS SELECT
When used in conjunction with the CREATE TABLE AS SELECT statement, selecting from an external table imports data into a table within the SQL pool. In addition to the COPY statement, external tables are useful for loading data.
Incorrect Answers:
CREATE EXTERNAL TABLE
The CREATE EXTERNAL TABLE command creates an external table for Synapse SQL to access data stored in Azure Blob Storage or Azure Data Lake Storage.
You need to design a data ingestion and storage solution for the Twitter feeds. The solution must meet the customer sentiment analytics requirements.
What should you include in the solution To answer, select the appropriate options in the answer area.
NOTE Each correct selection b worth one point.
Hot Area:
Correct Answer:
Box 1: Configure Evegent Hubs partitions
Scenario: Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.
Event Hubs is designed to help with processing of large volumes of events. Event Hubs throughput is scaled by using partitions and throughput-unit allocations.
Incorrect Answers:
Event Hubs Dedicated: Event Hubs clusters offer single-tenant deployments for customers with the most demanding streaming needs. This single-tenant offering has a guaranteed 99.99% SLA and is available only on our Dedicated pricing
tier.
Auto-Inflate: The Auto-inflate feature of Event Hubs automatically scales up by increasing the number of TUs, to meet usage needs. Event Hubs traffic is controlled by TUs (standard tier). Auto-inflate enables you to start small with the minimum required TUs you choose. The feature then scales automatically to the maximum limit of TUs you need, depending on the increase in your traffic.
Box 2: An Azure Data Lake Storage Gen2 account
Scenario: Ensure that the data store supports Azure AD-based access control down to the object level.
Azure Data Lake Storage Gen2 implements an access control model that supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs).
Incorrect Answers:
Azure Databricks: An Azure administrator with the proper permissions can configure Azure Active Directory conditional access to control where and when users are permitted to sign in to Azure Databricks.
Azure Storage supports using Azure Active Directory (Azure AD) to authorize requests to blob data. You can scope access to Azure blob resources at the following levels, beginning with the narrowest scope:
- An individual container. At this scope, a role assignment applies to all of the blobs in the container, as well as container properties and metadata.
-The storage account. At this scope, a role assignment applies to all containers and their blobs.
-
The resource group. At this scope, a role assignment applies to all of the containers in all of the storage accounts in the resource group.
-
The subscription. At this scope, a role assignment applies to all of the containers in all of the storage accounts in all of the resource groups in the subscription.
You need to implement an Azure Synapse Analytics database object for storing the sales transactions data. The solution must meet the sales transaction dataset requirements.
What solution must meet the sales transaction dataset requirements.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Box 1: Create table
Scenario: Load the sales transaction dataset to Azure Synapse Analytics
Box 2: RANGE RIGHT FOR VALUES
Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
RANGE RIGHT: Specifies the boundary value belongs to the partition on the right (higher values).
FOR VALUES ( boundary_value [,...n] ): Specifies the boundary values for the partition.
Scenario: Load the sales transaction dataset to Azure Synapse Analytics.
Contoso identifies the following requirements for the sales transaction dataset:
1.
Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
2.
Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
3.
Implement a surrogate key to account for changes to the retail store addresses.
4.
Ensure that data storage costs and performance are predictable.
5.
Minimize how long it takes to remove old records.
Question 14:
You are designing an Azure Data Lake Storage solution that will transform raw JSON files for use in an analytical workload.
You need to recommend a format for the transformed files. The solution must meet the following requirements:
1.
Contain information about the data types of each column in the files.
2.
Support querying a subset of columns in the files.
3.
Support read-heavy analytical workloads.
4.
Minimize the file size.
What should you recommend?
A. JSON
B. CSV
C. Apache Avro
D. Apache Parquet
Correct Answer: D
Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar format.
Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance.
It is especially good for queries that read particular columns from a “wide” (with many columns) table since only needed columns are read, and IO is minimized.
Incorrect:
Not C:
The Avro format is the ideal candidate for storing data in a data lake landing zone because:
1.
Data from the landing zone is usually read as a whole for further processing by downstream systems (the row-based format is more efficient in this case).
2.
Downstream systems can easily retrieve table schemas from Avro files (there is no need to store the schemas separately in an external meta store).
3.
Any source schema change is easily handled (schema evolution). Reference: https://www.clairvoyant.ai/blog/big-data-file-formats
Question 15:
You have an Azure Synapse Analytics dedicated SQL pool.
You need to Create a fact table named Table1 that will store sales data from the last three years. The solution must be optimized for the following query operations:
1.
Show order counts by week.
2.
Calculate sales totals by region.
3.
Calculate sales totals by product.
4.
Find all the orders from a given month. Which data should you use to partition Table1?
A. region
B. product
C. week
D. month
Correct Answer: D
Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column.
Benefits to queries Partitioning can also be used to improve query performance. A query that applies a filter to partitioned data can limit the scan to only the qualifying partitions. This method of filtering can avoid a full table scan and only scan a smaller subset of data. With the introduction of clustered columnstore indexes, the predicate elimination performance benefits are less beneficial, but in some cases there can be a benefit to queries.
For example, if the sales fact table is partitioned into 36 months using the sales date field, then queries that filter on the sale date can skip searching in partitions that don't match the filter.
Note: Benefits to loads The primary benefit of partitioning in dedicated SQL pool is to improve the efficiency and performance of loading data by use of partition deletion, switching and merging. In most cases data is partitioned on a date column that is closely tied to the order in which the data is loaded into the SQL pool. One of the greatest benefits of using partitions to maintain data is the avoidance of transaction logging. While simply inserting, updating, or deleting data can be the most straightforward approach, with a little thought and effort, using partitioning during your load process can substantially improve performance.
You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool named Pool1. Pool1 receives new data once every 24 hours. You have the following function.
You have the following query.
The query is executed once every 15 minutes and the @parameter value is set to the current date.
You need to minimize the time it takes for the query to return results.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Create an index on the avg_f column.
B. Convert the avg_c column into a calculated column.
C. Create an index on the sensorid column.
D. Enable result set caching.
E. Change the table distribution to replicate.
Correct Answer: BD
D: When result set caching is enabled, dedicated SQL pool automatically caches query results in the user database for repetitive use. This allows subsequent query executions to get results directly from the persisted cache so recomputation is not needed. Result set caching improves query performance and reduces compute resource usage. In addition, queries using cached results set do not use any concurrency slots and thus do not count against existing concurrency limits.
Incorrect:
Not A, not C: No joins so index not helpful.
Not E: What is a replicated table?
A replicated table has a full copy of the table accessible on each Compute node. Replicating a table removes the need to transfer data among Compute nodes before a join or aggregation. Since the table has multiple copies, replicated tables
work best when the table size is less than 2 GB compressed. 2 GB is not a hard limit. If the data is static and does not change, you can replicate larger tables.
You have an Azure Blob Storage account named blob1 and an Azure Data Factory pipeline named pipeline1.
You need to ensure that pipeline1 runs when a file is deleted from a container in blob1. The solution must minimize development effort.
Which type of trigger should you use?
A. schedule
B. storage event
C. tumbling window
D. custom event
Correct Answer: B
Explanation:
You can create a trigger that runs a pipeline in response to a storage event.
Event-driven architecture (EDA) is a common data integration pattern that involves production, detection, consumption, and reaction to events. Data integration scenarios often require customers to trigger pipelines based on events happening
in storage account, such as the arrival or deletion of a file in Azure Blob Storage account. Data Factory and Synapse pipelines natively integrate with Azure Event Grid, which lets you trigger pipelines on such events.
Note
The Storage Event Trigger currently supports only Azure Data Lake Storage Gen2 and General-purpose version 2 storage accounts.
What should you do to improve high availability of the real-time data processing solution?
A. Deploy a High Concurrency Databricks cluster.
B. 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.
C. Set Data Lake Storage to use geo-redundant storage (GRS).
D. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.
Correct Answer: D
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
You have an Azure Data Lake Storage Gen2 account named account1 and an Azure event hub named Hub1. Data is written to account1 by using Event Hubs Capture.
You plan to query account by using an Apache Spark pool in Azure Synapse Analytics.
You need to create a notebook and ingest the data from account1. The solution must meet the following requirements:
Retrieve multiple rows of records in their entirety.
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