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NEW QUESTION: 1
You have the following tables.

There is a many-to-one relationship from Subscriber to Date that uses Subscriber[StartDate] and Date
[Date]. The Cross filter direction of the relationship is set to Single.
You plan to create a column chart that displays the following two measures:
Count of SubscriberID by Month based on the StartDate

Count of SubscriberID by Month based on the EndDate

What should you do before you create the measures?
A. Change the active relationship for many-to-one.
B. Create an active one-to-one relationship from Subscriber[StartDate] to Date[Date].
C. Create an inactive many-to-one relationship from Subscriber[StartDate] to Date[Date].
D. Change the Cross filter direction of the active relationship to Both.
Answer: D
Explanation:
Explanation/Reference:
References: https://docs.microsoft.com/en-us/power-bi/desktop-create-and-manage-relationships

NEW QUESTION: 2
What assesses potential loss that could be caused by a disaster?
A. The Business Assessment (BA)
B. The Business Continuity Plan (BCP)
C. The Risk Assessment (RA)
D. The Business Impact Analysis (BIA)
Answer: D
Explanation:
Explanation/Reference:
The Business Assessment is divided into two components. Risk Assessment (RA) and Business Impact Analysis (BIA). Risk Assessment is designed to evaluate existing exposures from the organization's environment, whereas the BIA assesses potential loss that could be caused by a disaster. The Business Continuity Plan's goal is to reduce the risk of financial loss by improving the ability to recover and restore operations efficiently and effectively.
Source: BARNES, James C. & ROTHSTEIN, Philip J., A Guide to Business Continuity Planning, John Wiley & Sons, 2001 (page 57).
And: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, John Wiley & Sons, 2001, Chapter 8: Business Continuity Planning and Disaster Recovery Planning (page 276).

NEW QUESTION: 3
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
*Serve as a repository (or high volumes of large files in various formats.
*Implement optimized storage for big data analytics workloads.
*Ensure that data can be organized using a hierarchical structure.
Batch processing:
*Use a managed solution for in-memory computation processing.
*Natively support Scala, Python, and R programming languages.
*Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
*Support parallel processing.
*Use columnar storage.
*Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which 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

Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

NEW QUESTION: 4
Your customer wants to capture multiple GBs of aggregate real-time key performance indicators (KPIs) from their game servers running on Google Cloud Platform and monitor the KPIs with low latency. How should they capture the KPIs?
A. Store time-series data from the game servers in Google Bigtable, and view it using Google Data Studio.
B. Insert the KPIs into Cloud Datastore entities, and run ad hoc analysis and visualizations of them in Cloud Datalab.
C. Schedule BigQuery load jobs to ingest analytics files uploaded to Cloud Storage every ten minutes, and visualize the results in Google Data Studio.
D. Output custom metrics to Stackdriver from the game servers, and create a Dashboard in Stackdriver Monitoring Console to view them.
Answer: A
Explanation:
https://cloud.google.com/monitoring/api/v3/metrics-details#metric-kinds