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NEW QUESTION: 1
You decide to set up Cloud NAT. After completing the configuration, you find that one of your instances is not using the Cloud NAT for outbound NAT.
What is the most likely cause of this problem?
A. An external IP address has been configured on the instance.
B. You have created static routes that use RFC1918 ranges.
C. The instance is accessible by a load balancer external IP address.
D. The instance has been configured with multiple interfaces.
Answer: A
Explanation:
https://www.sovereignsolutionscorp.com/google-cloud-nat/
NEW QUESTION: 2
You are doing a load testing exercise on your application hosted on AWS. While testing your Amazon RDS MySQL DB instance, you notice that when you hit 100% CPU utilization on it, your application becomes non- responsive. Your application is read-heavy. What are methods to scale your data tier to meet the application's needs? Choose three answers from the options given below
A. Use an Amazon SQS queue to throttle data going to the Amazon RDS DB instance.
B. Add your Amazon RDS DB instance to an Auto Scalinggroup and configure your Cloud Watch metric based on CPU utilization.
C. Enable Multi-AZ for your Amazon RDS DB instance.
D. Add Amazon RDS DB read replicas, and have your application direct read queries to them.
E. Shard your data set among multiple Amazon RDS DB instances.
F. Use ElastiCache in front of your Amazon RDS DB to cache common queries.
Answer: D,E,F
Explanation:
Explanation
Amazon RDS Read Replicas provide enhanced performance and durability for database (DB) instances. This replication feature makes it easy to elastically scale out beyond the capacity constraints of a single DB Instance for read-heavy database workloads. You can create one or more replicas of a given source DB Instance and serve high-volume application read traffic from multiple copies of your data, thereby increasing aggregate read throughput For more information on Read Replica's please refer to the below link:
https://aws.amazon.com/rds/details/read-replicas/
Sharding is a common concept to split data across multiple tables in a database For more information on sharding please refer to the below link:
* https://forums.awsa mazon.com/thread jspa?messagelD=203052
Amazon OastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases Amazon OastiCache is an in-memory key/value store that sits between ycbetappiicipiJGra arcdalie data store (database) that it accesses. Whenever your application requests data, it first makes the request to the DastiCache cache. If the data exists in the cache and is current, OastiCache returns the data to your application. If the data does not exist in the cache, or the data in the cache has expired, your application requests the data from your data store which returns the data to your application. Your application then writes the data received from the store to the cache so it can be more quickly retrieved next time it is requested. For more information on Elastic Cache please refer to the below link:
* https://aws.amazon.com/elasticache/
Option B is not an ideal way to scale a database
Option C is not ideal to store the data which would go into a database because of the message size Option F is invalid because Multi-AZ feature is only a failover option
NEW QUESTION: 3
正しいテキスト
SalesforceとPCを統合している4つのデスクトップクライアントはどれですか。
Answer:
Explanation:
Outlookへの接続、Lotus Notesへの接続、Officeへの接続、オフラインでの接続
NEW QUESTION: 4
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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1.csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2i
1,1.2,0
2,1,1,
1 3,2.1,0
You run the following code:
You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:
Solution: Run the following code:
Does the solution meet the goal?
A. No
B. Yes
Answer: A