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Google Certified Professional Data Engineer Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
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?

A. BigQuery
B. Cloud Bigtable
C. Cloud SQL
D. Cloud Datastore

Answer: D

Explanation:
https://cloud.google.com/datastore/docs/concepts/overview

NEW QUESTION # 107
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
* No interaction by the user on the site for 1 hour
* Has added more than $30 worth of products to the basket
* Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

A. Use a global window with a time based trigger with a delay of 60 minutes.
B. Use a fixed-time window with a duration of 60 minutes.
C. Use a session window with a gap time duration of 60 minutes.
D. Use a sliding time window with a duration of 60 minutes.

Answer: A

NEW QUESTION # 108
How can you get a neural network to learn about relationships between categories in a categorical feature?

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

Answer: A

Explanation:
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.

NEW QUESTION # 109
What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and loading data from a file
B. Queries and streaming inserts
C. Storage, queries, and streaming inserts
D. Storage, queries, and exporting data

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 110
What are two of the benefits of using denormalized data structures in BigQuery?

A. Reduces the amount of data processed, increases query speed
B. Reduces the amount of data processed, reduces the amount of storage required
C. Increases query speed, makes queries simpler
D. Reduces the amount of storage required, increases query speed

Answer: C

Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses. Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 111
......

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Professional-Data-Engineer Customized Lab Simulation | Valid Professional-Data-Engineer Exam Simulator

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Google Certified Professional Data Engineer Exam Sample Questions (Q106-Q111):

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