- (Exam Topic 6)
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don’t get slots to execute their query and you need to correct this. You’d like to avoid introducing new projects to your account.
What should you do?

1. A. Convert your batch BQ queries into interactive BQ queries.
2. B. Create an additional project to overcome the 2K on-demand per-project quota.
3. C. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
4. D. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

Correct Answer: C
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

- (Exam Topic 5)
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

1. A. before
2. B. after
3. C. only if
4. D. once

Correct Answer: A
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

- (Exam Topic 5)
How can you get a neural network to learn about relationships between categories in a categorical feature?

1. A. Create a multi-hot column
2. B. Create a one-hot column
3. C. Create a hash bucket
4. D. Create an embedding column

Correct Answer: D
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn’t encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding
column. The idea is that each category has a smaller vector with, let’s say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference:
https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-dee

- (Exam Topic 1)
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

1. A. Issue a command to restart the database servers.
2. B. Retry the query with exponential backoff, up to a cap of 15 minutes.
3. C. Retry the query every second until it comes back online to minimize staleness of data.
4. D. Reduce the query frequency to once every hour until the database comes back online.

Correct Answer: B

- (Exam Topic 6)
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

1. A. Create a table in BigQuery, and append the new samples for CPU and memory to the table
2. B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
3. C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
4. D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Correct Answer: D