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| Section | Weight | Objectives |
|---|---|---|
| Implement a secure environment | 20–25% | - Configure authentication and authorization
|
| Monitor and optimize operational resources | 15–20% | - Automate maintenance and operations
|
| Optimize query performance | 15–20% | - Analyze query execution plans
|
| Plan and implement data platform resources | 15–20% | - Deploy database resources
|
| Plan and configure high availability and disaster recovery | 20–25% | - Implement backup and restore
|
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NEW QUESTION # 43
You have an Azure Databricks workspace named workspace1 in the Standard pricing tier. Workspace1 contains an all-purpose cluster named cluster1.
You need to reduce the time it takes for cluster1 to start and scale up. The solution must minimize costs.
What should you do first?
Answer: A
Explanation:
You can use Databricks Pools to Speed up your Data Pipelines and Scale Clusters Quickly.
Databricks Pools, a managed cache of virtual machine instances that enables clusters to start and scale 4 times faster.
Reference:
https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html Basic Concept: This question tests plan and configure a high availability and disaster recovery (ha/dr) environment in the DP-300 exam context. The correct choice is determined by the exact service boundary and operational requirement stated in the scenario.
Why C is Correct: Create a pool in workspace1. is part of the availability or recovery design space, but the correct choice must satisfy the specified failover, restore, quorum, RPO, or RTO requirement. The scenario asks for: You need to reduce the time it takes for cluster1 to start and scale up. That makes Create a pool in workspace1. the option that satisfies the required Azure SQL layer and operational outcome.
Why A is Wrong: Upgrade workspace1 to the Premium pricing tier. is part of the availability or recovery design space, but the correct choice must satisfy the specified failover, restore, quorum, RPO, or RTO requirement. It does not meet the failover, restore, quorum, or cross-region continuity target stated in the question, even if it is valid in a different availability design.
Why B is Wrong: Configure a global init script for workspace1. is part of the availability or recovery design space, but the correct choice must satisfy the specified failover, restore, quorum, RPO, or RTO requirement. It handles a different resilience pattern and would not deliver the failover or recovery behavior required here.
Why D is Wrong: Create a cluster policy in workspace1. is part of the availability or recovery design space, but the correct choice must satisfy the specified failover, restore, quorum, RPO, or RTO requirement. It does not meet the failover, restore, quorum, or cross-region continuity target stated in the question, even if it is valid in a different availability design.
NEW QUESTION # 44
You are designing an enterprise data warehouse in Azure Synapse Analytics that will store website traffic analytics in a star schema.
You plan to have a fact table for website visits. The table will be approximately 5 GB.
You need to recommend which distribution type and index type to use for the table. The solution must provide the fastest query performance.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-index
NEW QUESTION # 45
You configure version control for an Azure Data Factory instance as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE:Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
Box 1: adf_publish
By default, data factory generates the Resource Manager templates of the published factory and saves them into a branch called adf_publish. To configure a custom publish branch, add a publish_config.json file to the root folder in the collaboration branch. When publishing, ADF reads this file, looks for the field publishBranch, and saves all Resource Manager templates to the specified location. If the branch doesn't exist, data factory will automatically create it. And example of what this file looks like is below:
{
"publishBranch": "factory/adf_publish"
}
Box 2: /dwh_barchlet/ adf_publish/contososales
RepositoryName: Your Azure Repos code repository name. Azure Repos projects contain Git repositories to manage your source code as your project grows. You can create a new repository or use an existing repository that's already in your project.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/source-control
NEW QUESTION # 46
You have SQL Server 2019 on an Azure virtual machine that runs Windows Server 2019. The virtual machine has 4 vCPUs and 28 GB of memory.
You scale up the virtual machine to 16 vCPUSs and 64 GB of memory.
You need to provide the lowest latency for tempdb.
What is the total number of data files that tempdb should contain?
Answer: C
Explanation:
The number of files depends on the number of (logical) processors on the machine. As a general rule, if the number of logical processors is less than or equal to eight, use the same number of data files as logical processors. If the number of logical processors is greater than eight, use eight data files and then if contention continues, increase the number of data files by multiples of 4 until the contention is reduced to acceptable levels or make changes to the workload/code.
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/databases/tempdb-database
NEW QUESTION # 47
You plan to perform batch processing in Azure Databricks once daily.
Which type of Databricks cluster should you use?
Answer: A
Explanation:
Azure Databricks makes a distinction between all-purpose clusters and job clusters. You use all-purpose clusters to analyze data collaboratively using interactive notebooks. You use job clusters to run fast and robust automated jobs.
The Azure Databricks job scheduler creates a job cluster when you run a job on a new job cluster and
terminates the cluster when the job is complete.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/clusters
NEW QUESTION # 48
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