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

A.    Yes
B.    No

Answer: B
We need a High Concurrency cluster for the data engineers and 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.

You have an Azure Data Lake Storage account that contains a staging zone. You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.
Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that copies the data to a staging table in the data warehouse, and then uses a stored procedure to execute the R script.
Does this meet the goal?

A.    Yes
B.    No

Answer: A
If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity with your own data processing logic and use the activity in the pipeline. Note: You can use data transformation activities in Azure Data Factory and Synapse pipelines to transform and process your raw data into predictions and insights at scale.

You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1. You plan to insert data from the files in container1 into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1. You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.
Solution: You use an Azure Synapse Analytics serverless SQL pool to create an external table that has an additional DateTime column.
Does this meet the goal?

A.    Yes
B.    No

Answer: B
Instead use the derived column transformation to generate new columns in your data flow or to modify existing fields.

You build a data warehouse in an Azure Synapse Analytics dedicated SQL pool. Analysts write a complex SELECT query that contains multiple JOIN and CASE statements to transform data for use in inventory reports. The inventory reports will use the data and additional WHERE parameters depending on the report. The reports will be produced once daily. You need to implement a solution to make the dataset available for the reports. The solution must minimize query times. What should you implement?

A.    an ordered clustered columnstore index
B.    a materialized view
C.    result set caching
D.    a replicated table

Answer: B
Materialized views for dedicated SQL pools in Azure Synapse provide a low maintenance method for complex analytical queries to get fast performance without any query change.
Not C: One daily execution does not make use of result cache caching. Note: 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.

You have an Azure Synapse Analytics workspace named WS1 that contains an Apache Spark pool named Pool1. You plan to create a database named DB1 in Pool1. You need to ensure that when tables are created in DB1, the tables are available automatically as external tables to the built-in serverless SQL pool. Which format should you use for the tables in DB1?

A.    CSV
B.    ORC
C.    JSON
D.    Parquet

Answer: D
Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools. For each Spark external table based on Parquet or CSV and located in Azure Storage, an external table is created in a serverless SQL pool database.

You are planning a solution to aggregate streaming data that originates in Apache Kafka and is output to Azure Data Lake Storage Gen2. The developers who will implement the stream processing solution use Java. Which service should you recommend using to process the streaming data?

A.    Azure Event Hubs
B.    Azure Data Factory
C.    Azure Stream Analytics
D.    Azure Databricks

Answer: D

You are designing a partition strategy for a fact table in an Azure Synapse Analytics dedicated SQL pool. The table has the following specifications:
– Contain sales data for 20,000 products.
– Use hash distribution on a column named ProductID.
– Contain 2.4 billion records for the years 2019 and 2020.
Which number of partition ranges provides optimal compression and performance for the clustered columnstore index?

A.    40
B.    240
C.    400
D.    2,400

Answer: A

You are implementing a batch dataset in the Parquet format. Data files will be produced be using Azure Data Factory and stored in Azure Data Lake Storage Gen2. The files will be consumed by an Azure Synapse Analytics serverless SQL pool. You need to minimize storage costs for the solution. What should you do?

A.    Use Snappy compression for files.
B.    Use OPENROWSET to query the Parquet files.
C.    Create an external table that contains a subset of columns from the Parquet files.
D.    Store all data as string in the Parquet files.

Answer: C
An external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.

You are designing a data mart for the human resources (HR) department at your company. The data mart will contain employee information and employee transactions. From a source system, you have a flat extract that has the following fields:
– EmployeeID
– FirstName
– LastName
– Recipient
– GrossAmount
– TransactionID
– GovernmentID
– NetAmountPaid
– TransactionDate
You need to design a star schema data model in an Azure Synapse Analytics dedicated SQL pool for the data mart. Which two tables should you create? (Each correct answer presents part of the solution. Choose two.)

A.    a dimension table for Transaction
B.    a dimension table for EmployeeTransaction
C.    a dimension table for Employee
D.    a fact table for Employee
E.    a fact table for Transaction

Answer: CE
C: Dimension tables contain attribute data that might change but usually changes infrequently. For example, a customer’s name and address are stored in a dimension table and updated only when the customer’s profile changes. To minimize the size of a large fact table, the customer’s name and address don’t need to be in every row of a fact table. Instead, the fact table and the dimension table can share a customer ID. A query can join the two tables to associate a customer’s profile and transactions.
E: Fact tables contain quantitative data that are commonly generated in a transactional system, and then loaded into the dedicated SQL pool. For example, a retail business generates sales transactions every day, and then loads the data into a dedicated SQL pool fact table for analysis.

You use Azure Stream Analytics to receive data from Azure Event Hubs and to output the data to an Azure Blob Storage account. You need to output the count of records received from the last five minutes every minute. Which windowing function should you use?

A.    Session
B.    Tumbling
C.    Sliding
D.    Hopping

Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap and be emitted more often than the window size. Events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.

You have the following Azure Data Factory pipelines:
– Ingest Data from System1
– Ingest Data from System2
– Populate Dimensions
– Populate Facts
Ingest Data from System1 and Ingest Data from System2 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System2. Populate Facts must execute after Populate Dimensions pipeline. All the pipelines must execute every eight hours. What should you do to schedule the pipelines for execution?

A.    Add an event trigger to all four pipelines.
B.    Add a schedule trigger to all four pipelines.
C.    Create a patient pipeline that contains the four pipelines and use a schedule trigger.
D.    Create a patient pipeline that contains the four pipelines and use an event trigger.

Answer: C
Schedule trigger: A trigger that invokes a pipeline on a wall-clock schedule.

You are monitoring an Azure Stream Analytics job by using metrics in Azure. You discover that during the last 12 hours, the average watermark delay is consistently greater than the configured late arrival tolerance. What is a possible cause of this behavior?

A.    Events whose application timestamp is earlier than their arrival time by more than five minutes arrive as inputs.
B.    There are errors in the input data.
C.    The late arrival policy causes events to be dropped.
D.    The job lacks the resources to process the volume of incoming data.

Answer: D
Watermark Delay indicates the delay of the streaming data processing job. There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:
1. Not enough processing resources in Stream Analytics to handle the volume of input events. To scale up resources, see Understand and adjust Streaming Units.
2. Not enough throughput within the input event brokers, so they are throttled. For possible solutions, see Automatically scale up Azure Event Hubs throughput units.
3. Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.

You are designing an Azure Databricks cluster that runs user-defined local processes. You need to recommend a cluster configuration that meets the following requirements:
– Minimize query latency.
– Maximize the number of users that can run queries on the cluster at the same time.
– Reduce overall costs without compromising other requirements.
Which cluster type should you recommend?

A.    Standard with Auto Termination
B.    High Concurrency with Autoscaling
C.    High Concurrency with Auto Termination
D.    Standard with Autoscaling

Answer: B
A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies. Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don’t need to provision the cluster to match a workload.
Not C: The cluster configuration includes an auto terminate setting whose default value depends on cluster mode:
– Standard and Single Node clusters terminate automatically after 120 minutes by default.
– High Concurrency clusters do not terminate automatically by default.

You are building an Azure Synapse Analytics dedicated SQL pool that will contain a fact table for transactions from the first half of the year 2020. You need to ensure that the table meets the following requirements:
– Minimizes the processing time to delete data that is older than 10 years.
– Minimizes the I/O for queries that use year-to-date values.
How should you complete the Transact-SQL statement? (To answer, select the appropriate options in the answer area.)


Drag and Drop
You have data stored in thousands of CSV files in Azure Data Lake Storage Gen2. Each file has a header row followed by a properly formatted carriage return (/r) and line feed (/n). You are implementing a pattern that batch loads the files daily into an enterprise data warehouse in Azure Synapse Analytics by using PolyBase. You need to skip the header row when you import the files into the data warehouse. Before building the loading pattern, you need to prepare the required database objects in Azure Synapse Analytics. 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.)



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