Google Cloud Platform Big Data and Machine Learning Fundamentals complete course is currently being offered by Google Cloud through Coursera platform.
About this Course
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Skills You Will Gain
- Tensorflow
- Bigquery
- Google Cloud Platform
- Cloud Computing
Also Check: How to Apply for Coursera Financial Aid
Google Cloud Platform Big Data and Machine Learning Fundamentals Week 1 Quiz Answers - Coursera!
Module Review Quiz 1 Answers
Q1) What are the common big data challenges that you will be building solutions for in this course? (check all that apply)
Migrating existing on-premise workloads to the cloud
Analyzing large datasets at scale
Building streaming data pipelines
Applying machine learning to your datasets
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)
A defined Organization
Folders for teams and/or products
A defined access control policy with Cloud IAM
Q3) Which of the following is NOT one of the advantages of Google Cloud security
Google Cloud will automatically manage and curate your content and access policies to be safe for the public
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 ?
Practice with the datasets in the Google Cloud Public Datasets program
Find other public datasets online and upload them into BigQuery
Work to create your own dataset and then upload it into BigQuery for analysis
Q5) As you saw in the demo, Compute Engine nodes on GCP are:
Allocated on demand, and you pay for the time that they are up.
Module Review Quiz 2 Answers
Q1) Complete the following:
You should feed your machine learning model your _______ and not your _______. It will learn those for itself!
data, rules
Q2)True or False: Cloud SQL is a big data analytics warehouse
False
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.
False
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
Use PVMs or Preemptible Virtual Machines
Migrate your storage from on-cluster HDFS to off-cluster Google Cloud Storage (GCS)
Consider having multiple Cloud Dataproc instances for each priority workload and then turning them down when not in use
Q5) Google Cloud Storage is a good option for storing data that:
May be imported from a bucket into a Hadoop cluster for analysis
May be required to be read at some later time (i.e. load a CSV file into BigQuery)
Q6) Relational databases are a good choice when you need:
Transactional updates on relatively small datasets
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?
Running it on Google infrastructure offers reliability and cost savings
Fully-managed versions of the software offer no-ops
Module Review Quiz 3 Answers
Q1) Which of the below are the core services that make up BigQuery?
(choose the correct 2)
Query service
Storage service
Q2)You want to know how many rows are in the BigQuery Public Dataset on San Francisco Bike Shares. What could you do?
# Run the below query:
SELECT
COUNT(*) AS total_trips
FROM
`bigquery-public-data.san_francisco_bikeshare.bikeshare_trips`
In the BigQuery Web UI, find the table and click the details tab and view the rows.
Q3) True or False: You can query a Google Spreadsheet directly from BigQuery without loading it in first.
True
Q4) 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)
Consider adding lat / long geographic data points as new columns and using GIS Functions to quickly plot the distances your fleet has travelled.
Consider making ride_timestamp an ARRAY of timestamp values so each ride_id row in your table could still be unique and easy to report off of.
Q5) 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.
instance or observation
feature
labels
Google Cloud Platform Big Data and Machine Learning Fundamentals Week 2 Quiz Answers - Coursera!
Module Review Quiz 4 Answers
Q1) Relational databases are a good choice when you need:
Transactional updates on relatively small datasets
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?
Fully-managed versions of the software offer no-ops
Running it on Google infrastructure offers reliability and cost savings
Module Review Quiz 5 Answers
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?
Try the Cloud Vision API first
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