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This Google Cloud Platform Big Data and Machine Learning Fundamentals Quiz Answers introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities.
This course showcases the ease, flexibility, and power of big data solutions on Google Cloud. It is a great course suitable for any beginner without prior experience and knowledge about the Google Cloud Platform. It is aimed at those who are interested in Big Data and Machine Learning.
Some of the Big Data and Machine Learning products in GCP are explored and learned about BigQuery using SQL, Cloud Data Pub/Sub, AutoML Vision, and lastly about how to build a Tensorflow model. The hands-on labs given are helpful and gave step-by-step instructions to the user.
The videos are designed to teach someone who is not familiar with common query language, SQL, data modeling, and Machine Learning. It is totally fun and enjoyable throughout the learning journey. Can’t wait to learn more and deeper about Machine Learning with Tensorflow in GCP or the specialization for Big Data and Machine Learning!
Q1. What are the common big data challenges that you will be building solutions for in this course? (check all that apply)
Q2. You have a large enterprise that will likely have many teams using their own Google Cloud Platform projects and resources. What should you be sure to have to help manage and administer these resources? (check all that apply)
Q3. Which of the following is NOT one of the advantages of Google Cloud security.
Q4. If you don’t have a large dataset of your own but still want to practice writing queries and building pipelines on Google Cloud Platform, what should you do?
Q5. As you saw in the demo, Compute Engine nodes on GCP are:
Q1. Complete the following:
You should feed your machine learning model your and not your. It will learn those for itself.
Q2. True or False: Cloud SQL is a big data analytics warehouse.
Q3. True or False: If you are migrating your Hadoop workload to the cloud, you must first rewrite all your Spark jobs to be compliant with the cloud.
Q4. You are thinking about migrating your Hadoop workloads to the cloud and you have a few workloads that are fault-tolerant (they can handle interruptions of individual VMs gracefully). What are some architecture considerations you should explore in the cloud? (Choose all that apply)
Q5. Google Cloud Storage is a good option for storing data that: (Select the 2 correct options below).
Q6. Relational databases are a good choice when you need:
Q7. Cloud SQL and Cloud Dataproc offer familiar tools (MySQL and Hadoop/Pig/Hive/Spark). What is the value-add provided by Google Cloud Platform? (Select the 2 correct options below )
Q1. Which of the below are the core services that makeup BigQuery? (choose the correct 2)
Q2. You want to know how many rows are in the BigQuery Public Dataset on San Francisco Bike Shares. What could you do?
Only Correct Answers Listed Below
Run the below query:
COUNT(*) AS total_trips
Q3. You have a taxi service data schema that has three columns: ride_id ride_timestamp ride_status You want to use BigQuery for reporting but you don’t want to split your table into multiple sub-tables. What native features of BigQuery data types should you explore? (check all that apply)
Q4. Complete the following: In ML, a row of data is called a(n) and a column of data is called a(n) _. We mark one or more columns as which we know for historical data and are trying to predict for future data.
Q1. Relational databases are a good choice when you need:
Q2. Cloud SQL and Cloud Dataproc offer familiar tools (MySQL and Hadoop/Pig/Hive/Spark). What is the value-add provided by Google Cloud Platform? (Select all of the correct options)
Q1. If you have an image classification task for identifying whether a car is present in a photo or not, which solution should you try first?
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