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NEW QUESTION: 1
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 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, Spark, 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 Databrick clusters for the workloads.
Solution: You create a High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
No need for a High Concurrency cluster for each data scientist.
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.
References:
https://docs.azuredatabricks.net/clusters/configure.html

NEW QUESTION: 2
To create policy that tests only for the authentication error of expired_credentials, can you use the VPM, CPL, or either?
A. CPL
B. VPM
C. Either
Answer: B

NEW QUESTION: 3
고객은 단일 시스템에서 로그 스트림 (액세스 로그 응용 프로그램 로그 보안 로그 등)을 통합하고자합니다. 일단 통합되면 고객은 지능형을 기반으로 이러한 로그를 실시간으로 분석하려고합니다. 고객은 추론을 검증해야 할 필요가 있습니다. 지난 12 시간 동안 추출한 데이터 샘플로 돌아가는 것이 필요합니다.
고객의 요구 사항을 충족시키는 가장 좋은 방법은 무엇입니까?
A. 사용자 정의 로그를 수신하도록 Amazon Cloud Trail을 구성하고, EMR을 사용하여 로그를 발견 적 방식으로 적용합니다.
B. EC2 syslogd 서버의 Auto Scaling 그룹을 설정하고, S3에 EMR을 사용하여 로그를 저장하여 로그에 발견 적 방법을 적용합니다
C. 모든 로그 이벤트를 Amazon SQS로 보냅니다. EC2 서버의 Auto Scaling 그룹을 설정하여 로그를 소비하고 경험적 방법을 적용합니다.
D. 모든 로그 이벤트를 Amazon에 전송 Kinesis 클라이언트 프로세스를 개발하여 로그에 추론을 적용합니다.
Answer: D

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