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Google Professional-Data-Engineer Exam Sample Questions

Question # 1

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

A. Linear regression

B. Logistic classification

C. Recurrent neural network

D. Feedforward neural network

Answer

A.

Linear regression

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Question # 2

You need to compose visualizations for operations teams with the following requirements: Which approach meets the requirements?

A. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

D. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

Answer

C.

Load the data into Google Cloud Datastore tables, write a Google App Engine
Application that queries all rows, applies a function to derive the metric, and then renders
results in a table using the Google charts and visualization API.

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Question # 3

Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

A. categorical_column_with_vocabulary_list

B. categorical_column_with_hash_bucket

C. categorical_column_with_unknown_values

D. sparse_column_with_keys

Answer

B.

categorical_column_with_hash_bucket

If you know the set of all possible feature values of a column and there are only a few of them, you can use
categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID
starting from 0.
What if we don't know the set of possible values in advance? Not a problem. We can use
categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature
column occupation will be hashed to an integer ID as we encounter them in training.

---

Question # 4

Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error. SELECT person FROM `project1.example.table1` WHERE city = "London" How would you correct the error?

A. Add ", UNNEST(person)" before the WHERE clause.

B. Change "person" to "person.city".

C. Change "person" to "city.person". Add ", UNNEST(city)" before the WHERE clause

Answer

A.

Add ", UNNEST(person)" before the WHERE clause.

Explanation
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma

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Question # 5

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

A. The wide model is used for memorization, while the deep model is used for generalization.

B. A good use for the wide and deep model is a recommender system.

C. The wide model is used for generalization, while the deep model is used for memorization.

D. A good use for the wide and deep model is a small-scale linear regression problem.

Answer

A.

The wide model is used for memorization, while the deep model is used for generalization.

B.

A good use for the wide and deep model is a recommender system.

Explanation
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.

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