Google Cloud API Exam Questions and Answers

What is Google Cloud Dataproc? - ANSWER-Cloud Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Cloud Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. What are the open source data processing services that ship with Google Dataproc cluster servers? - ANSWER-Apache Hadoop, Apache Spark, Apache Pig, and Apache Hive Why scale a Cloud Dataproc cluster? - ANSWER-After creating a Cloud Dataproc cluster, you can adjust ("scale") the cluster by increasing or decreasing the number of worker nodes in the cluster. You can scale a Cloud Dataproc cluster at any time, even when jobs are running on the cluster. - to increase the number of workers to make a job run faster - to decrease the number of workers to save money - to increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage What is the default replication factor for data proc? - ANSWER-2 How many languages does Google Translate support? - ANSWER-104 What does Language Detection mean? - ANSWER-Sometimes you don't know your source text language in advance. For example, user generated content might not contain a language code. Translation API can automatically identify languages with high accuracy. What is the difference between the normal and premium edition of Google translate? - ANSWER-The API is available in two editions. The Premium edition is tailored for users who need precise, longform translation services. Examples include livestream translations, high volume of emails, and detailed articles and documents. The Standard edition offers translation in over 100 languages and price performance that is ideal for short, real-time conversational text. The normal edition uses phrase based translation. Phrase based translation translates based on a phrase or a sentence. The premium edition uses neural machine translation which reduces translation errors by 55-85 percent. The neural machine translation uses context around the entire word when translating.