- (Exam Topic 5)
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?

1. A. Workers
2. B. Masters, workers, and parameter servers
3. C. Workers and parameter servers
4. D. Parameter servers

Correct Answer: C
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node. You may set TrainingInput.workerCount to specify the number of workers to use.
You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.
You can specify the type of machine for the master node, but you can't specify more than one master node. Reference: https://cloud.google.com/ml-engine/docs/training-overview#job_configuration_parameters

- (Exam Topic 5)
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

1. A. Include multiple time series values within the row key
2. B. Keep the row keep as an 8 bit integer
3. C. Keep your row key reasonably short
4. D. Keep your row key as long as the field permits

Correct Answer: C
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

- (Exam Topic 6)
An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

1. A. Create and share an authorized view that provides the aggregate results.
2. B. Create and share a new dataset and view that provides the aggregate results.
3. C. Create and share a new dataset and table that contains the aggregate results.
4. D. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Correct Answer: D
Reference: https://cloud.google.com/bigquery/docs/access-control

- (Exam Topic 6)
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

1. A. Enable data access logs in each Data Analyst’s projec
2. B. Restrict access to Stackdriver Logging via Cloud IAM roles.
3. C. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts’ project
4. D. Restrict access to the Cloud Storage bucket.
5. E. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit log
6. F. Restrict access to the project with the exported logs.
7. G. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit log
8. H. Restrict access to the project that contains the exported logs.

Correct Answer: D

- (Exam Topic 5)
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

1. A. gcloud ml-engine local train
2. B. gcloud ml-engine jobs submit training
3. C. gcloud ml-engine jobs submit training local
4. D. You can't run a TensorFlow program on your own computer using Cloud ML Engine .

Correct Answer: A
gcloud ml-engine local train - run a Cloud ML Engine training job locally
This command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job.
This is especially useful in the case of testing distributed models, as it allows you to validate that you are
properly interacting with the Cloud ML Engine cluster configuration. Reference: https://cloud.google.com/sdk/gcloud/reference/ml-engine/local/train