- (Exam Topic 6)
You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?

1. A. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
2. B. Place the MariaDB instances in an Instance Group with a Health Check.
3. C. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.
4. D. Install the StackDriver Agent and configure the MySQL plugin.

Correct Answer: C

- (Exam Topic 6)
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

1. A. Convert all daily log tables into date-partitioned tables
2. B. Convert the sharded tables into a single partitioned table
3. C. Enable query caching so you can cache data from previous months
4. D. Create separate views to cover each month, and query from these views

Correct Answer: A

- (Exam Topic 5)
When a Cloud Bigtable node fails, is lost.

1. A. all data
2. B. no data
3. C. the last transaction
4. D. the time dimension

Correct Answer: B
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

- (Exam Topic 5)
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

1. A. The wide model is used for memorization, while the deep model is used for generalization.
2. B. A good use for the wide and deep model is a recommender system.
3. C. The wide model is used for generalization, while the deep model is used for memorization.
4. D. A good use for the wide and deep model is a small-scale linear regression problem.

Correct Answer: AB
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

- (Exam Topic 1)
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

1. A. Cloud SQL
2. B. BigQuery
3. C. Cloud Bigtable
4. D. Cloud Datastore

Correct Answer: A