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What is the duration, language, and format of Google Professional Data Engineer Exam

  • Number of Questions: 50-60
  • Passing score: 80%
  • Language: English (U.S.), Japanese, Spanish, and Portuguese
  • Length of Examination: 120 minutes

Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

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Google Certified Professional Data Engineer Exam Sample Questions (Q242-Q247):

When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

  • A. node
  • B. type
  • C. zone
  • D. label

Answer: C

At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.
Reference: example#create_a_new_cloud_dataproc_cluste

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:
What is the most likely cause of the delay for this query?

  • A. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
  • B. Users are running too many concurrent queries in the system
  • C. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
  • D. The [myproject:mydataset.mytable] table has too many partitions

Answer: A

Why do you need to split a machine learning dataset into training data and test data?

  • A. So you can use one dataset for a wide model and one for a deep model
  • B. To make sure your model is generalized for more than just the training data
  • C. To allow you to create unit tests in your code
  • D. So you can try two different sets of features

Answer: B

The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.

Which of the following is NOT one of the three main types of triggers that Dataflow supports?

  • A. Trigger based on element count
  • B. Trigger that is a combination of other triggers
  • C. Trigger based on time
  • D. Trigger based on element size in bytes

Answer: D

There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference:

Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?

  • A. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
  • B. Put the data into Google Cloud Storage.
  • C. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
  • D. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

Answer: A



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