NEW QUESTION 1

You plan to develop a dataset named Purchases by using Azure databricks Purchases will contain the following columns:
• ProductID
• ItemPrice
• lineTotal
• Quantity
• StorelD
• Minute
• Month
• Hour
• Year
• Day
You need to store the data to support hourly incremental load pipelines that will vary for each StoreID. the solution must minimize storage costs. How should you complete the rode? To answer, select the appropriate options In the answer area.
NOTE: Each correct selection is worth one point.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 2

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 queues 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. Standard with Autoscaling
• C. High Concurrency with Autoscaling
• D. High Concurrency with Auto Termination

Answer: C

Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/clusters/configure

NEW QUESTION 3

A company uses Azure Stream Analytics to monitor devices.
The company plans to double the number of devices that are monitored.
You need to monitor a Stream Analytics job to ensure that there are enough processing resources to handle the additional load.
Which metric should you monitor?

• A. Early Input Events
• B. Late Input Events
• C. Watermark delay
• D. Input Deserialization Errors

Answer: A

Explanation:
There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:
Not enough processing resources in Stream Analytics to handle the volume of input events.
Not enough throughput within the input event brokers, so they are throttled.
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.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-time-handling

NEW QUESTION 4

You have an Azure Data Lake Storage Gen2 container that contains 100 TB of data.
You need to ensure that the data in the container is available for read workloads in a secondary region if an outage occurs in the primary region. The solution must minimize costs.
Which type of data redundancy should you use?

• A. zone-redundant storage (ZRS)
• B. read-access geo-redundant storage (RA-GRS)
• C. locally-redundant storage (LRS)
• D. geo-redundant storage (GRS)

Answer: B

Explanation:
Geo-redundant storage (with GRS or GZRS) replicates your data to another physical location in the secondary region to protect against regional outages. However, that data is available to be read only if the customer or Microsoft initiates a failover from the primary to secondary region. When you enable read access to the secondary region, your data is available to be read at all times, including in a situation where the primary region becomes unavailable.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/storage-redundancy

NEW QUESTION 5

You have an Azure data factory named ADM that contains a pipeline named Pipelwe1 Pipeline! must execute every 30 minutes with a 15-minute offset.
Vou need to create a trigger for Pipehne1. The trigger must meet the following requirements:
• Backfill data from the beginning of the day to the current time.
• If Pipeline1 fairs, ensure that the pipeline can re-execute within the same 30-mmute period.
• Ensure that only one concurrent pipeline execution can occur.
• Minimize de4velopment and configuration effort Which type of trigger should you create?

• A. schedule
• B. event-based
• C. manual
• D. tumbling window

Answer: A

NEW QUESTION 6

You have an Azure subscription that contains an Azure Data Lake Storage account named myaccount1. The myaccount1 account contains two containers named container1 and contained. The subscription is linked to an Azure Active Directory (Azure AD) tenant that contains a security group named Group1.
You need to grant Group1 read access to contamer1. The solution must use the principle of least privilege. Which role should you assign to Group1?

• A. Storage Blob Data Reader for container1
• B. Storage Table Data Reader for container1
• C. Storage Blob Data Reader for myaccount1
• D. Storage Table Data Reader for myaccount1

Answer: A

NEW QUESTION 7

You have an enterprise data warehouse in Azure Synapse Analytics.
You need to monitor the data warehouse to identify whether you must scale up to a higher service level to accommodate the current workloads
Which is the best metric to monitor?
More than one answer choice may achieve the goal. Select the BEST answer.

• A. Data 10 percentage
• B. CPU percentage
• C. DWU used
• D. DWU percentage

Answer: C

NEW QUESTION 8

You have an Azure data solution that contains an enterprise data warehouse in Azure Synapse Analytics named DW1.
Several users execute ad hoc queries to DW1 concurrently. You regularly perform automated data loads to DW1.
You need to ensure that the automated data loads have enough memory available to complete quickly and
successfully when the adhoc queries run. What should you do?

• A. Hash distribute the large fact tables in DW1 before performing the automated data loads.
• B. Assign a smaller resource class to the automated data load queries.
• C. Assign a larger resource class to the automated data load queries.
• D. Create sampled statistics for every column in each table of DW1.

Answer: C

Explanation:
The performance capacity of a query is determined by the user's resource class. Resource classes are
pre-determined resource limits in Synapse SQL pool that govern compute resources and concurrency for query execution.
Resource classes can help you configure resources for your queries by setting limits on the number of queries that run concurrently and on the compute-resources assigned to each query. There's a trade-off between memory and concurrency.
Smaller resource classes reduce the maximum memory per query, but increase concurrency. Larger resource classes increase the maximum memory per query, but reduce concurrency. Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/resource-classes-for-workload-ma

NEW QUESTION 9

You need to create an Azure Data Factory pipeline to process data for the following three departments at your company: Ecommerce, retail, and wholesale. The solution must ensure that data can also be processed for the entire company.
How should you complete the Data Factory data flow script? To answer, drag the appropriate values to the correct targets. Each value 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.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 10

You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. Table1 contains the following:
One billion rows
A clustered columnstore index
A hash-distributed column named Product Key
A column named Sales Date that is of the date data type and cannot be null Thirty million rows will be added to Table1 each month.
You need to partition Table1 based on the Sales Date column. The solution must optimize query performance and data loading.
How often should you create a partition?

• A. once per month
• B. once per year
• C. once per day
• D. once per week

Answer: B

Explanation:
Need a minimum 1 million rows per distribution. Each table is 60 distributions. 30 millions rows is added each month. Need 2 months to get a minimum of 1 million rows per distribution in a new partition.
Note: When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated
SQL pool already divides each table into 60 distributions.
Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a dedicated SQL pool has 60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partitio

NEW QUESTION 11

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 an Azure Storage account that contains 100 GB of files. The files contain text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.
You plan to copy the data from the storage account to an Azure SQL data warehouse. You need to prepare the files to ensure that the data copies quickly.
Solution: You modify the files to ensure that each row is less than 1 MB. Does this meet the goal?

• A. Yes
• B. No

Answer: A

Explanation:
When exporting data into an ORC File Format, you might get Java out-of-memory errors when there are large text columns. To work around this limitation, export only a subset of the columns.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

NEW QUESTION 12

You have an Azure Synapse Analytics workspace named WS1 that contains an Apache Spark pool named
Pool1.
You plan to create a database named D61 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 pod.
Which format should you use for the tables in DB1?

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

Answer: A

Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-storage-files-spark-tables

NEW QUESTION 13

What should you recommend using to secure sensitive customer contact information?

• A. data labels
• B. column-level security
• C. row-level security
• D. Transparent Data Encryption (TDE)

Answer: B

Explanation:
Scenario: All cloud data must be encrypted at rest and in transit.
Always Encrypted is a feature designed to protect sensitive data stored in specific database columns from
access (for example, credit card numbers, national identification numbers, or data on a need to know basis). This includes database administrators or other privileged users who are authorized to access the database to perform management tasks, but have no business need to access the particular data in the encrypted columns. The data is always encrypted, which means the encrypted data is decrypted only for processing by client applications with access to the encryption key.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-security-overview

NEW QUESTION 14

You have an Azure subscription that contains a logical Microsoft SQL server named Server1. Server1 hosts an Azure Synapse Analytics SQL dedicated pool named Pool1.
You need to recommend a Transparent Data Encryption (TDE) solution for Server1. The solution must meet the following requirements:
Track the usage of encryption keys.
Maintain the access of client apps to Pool1 in the event of an Azure datacenter outage that affects the availability of the encryption keys.
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

• A. Yes
• B. Not Mastered

Answer: A

NEW QUESTION 15

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You 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

Explanation:
Instead use the derived column transformation to generate new columns in your data flow or to modify existing fields.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-derived-column

NEW QUESTION 16
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