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Google Professional-Data-Engineer Exam Sample Questions


Question # 1

Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

A.

Field promotion

B.

Randomization

C.

Salting

D.

Hashing



A.

Field promotion


By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make
it easier to design a row key that facilitates queries





Question # 2

What are two of the benefits of using denormalized data structures in BigQuery?

A.

Reduces the amount of data processed, reduces the amount of storage required

B.

Increases query speed, makes queries simpler

C.

Reduces the amount of storage required, increases query speed

D.

Reduces the amount of data processed, increases query speed



B.

Increases query speed, makes queries simpler


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.





Question # 3

Which Java SDK class can you use to run your Dataflow programs locally?

A.

LocalRunner

B.

DirectPipelineRunner

C.

MachineRunner

D.

LocalPipelineRunner



B.

DirectPipelineRunner


DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests





Question # 4

If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

A.

Unsupervised learning

B.

Regressor

C.

Classifier

D.

Clustering estimator



B.

Regressor


Regression is the supervised learning task for modeling and predicting continuous, numeric variables.
Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples
include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based
on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items
in e-commerce, and social network analysis.





Question # 5

An external customer provides you with a daily dump of data from their database. The data
flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want
to analyze this data in Google BigQuery, but the data could have rows that are formatted
incorrectly or corrupted. How should you build this pipeline?

A.

Use federated data sources, and check data in the SQL query.

B.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

C.

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

D.

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push
errors to another dead-letter table for analysis.



D.

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push
errors to another dead-letter table for analysis.





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