- (Exam Topic 5)
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

1. A. ParDo
2. B. Sink API
3. C. Source API
4. D. Data extraction

Correct Answer: A
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

- (Exam Topic 5)
Why do you need to split a machine learning dataset into training data and test data?

1. A. So you can try two different sets of features
2. B. To make sure your model is generalized for more than just the training data
3. C. To allow you to create unit tests in your code
4. D. So you can use one dataset for a wide model and one for a deep model

Correct Answer: B
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/

- (Exam Topic 6)
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? Choose 2 answers.

1. A. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
2. B. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
3. C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
4. D. Write an application that uses Cloud Datastore client libraries to read all the entitie
5. E. Treat each entity as a BigQuery table row via BigQuery streaming inser
6. F. Assign an export timestamp for each export, and attach it as an extra column for each ro
7. G. Make sure that the BigQuery table is partitioned using the export timestamp column.
8. H. Write an application that uses Cloud Datastore client libraries to read all the entitie
9. I. Format the exported data into a JSON fil
10. J. Apply compression before storing the data in Cloud Source Repositories.

Correct Answer: CE

- (Exam Topic 6)
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

1. A. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.Deploy the models using Cloud Datapro
2. B. Call the model from your application.
3. C. Build and train a classification model with Spark MLlib to generate label
4. D. Build and train a second classification model with Spark MLlib to filter results to match customer preference
5. E. Deploy theModels using Cloud Datapro
6. F. Call the models from your application.
7. G. Build an application that calls the Cloud Video Intelligence API to generate label
8. H. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.
9. I. Build an application that calls the Cloud Video Intelligence API to generate label
10. J. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.

Correct Answer: C

- (Exam Topic 5)
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) .

1. A. VPN connection
2. B. Special browser
3. C. SSH tunnel
4. D. FTP connection

Correct Answer: C
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
Reference:
https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#connecting_to_the_web_interfaces