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Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

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The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing systems design, data modeling, data ingestion, data transformation, data storage, data analysis, and machine learning. You will be tested on your ability to design and implement data processing systems using Google Cloud technologies such as BigQuery, Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub. Professional-Data-Engineer Exam also covers best practices for data security and compliance, as well as troubleshooting and optimization techniques. Passing Professional-Data-Engineer exam requires a strong understanding of cloud computing principles and a solid grasp of data engineering concepts, making it a challenging but rewarding certification to earn.

Google Certified Professional Data Engineer Exam Sample Questions (Q269-Q274):

NEW QUESTION # 269
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Option C
B. Option A
C. Option B.
D. Option D

Answer: B

NEW QUESTION # 270
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use K-means Clustering to detect faces in the pixels.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Answer: D

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning.
So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 271
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. Users are running too many concurrent queries in the system
B. Most rows in the [myproject:mydataset.mytable]table have the same value in the country column, causing data skew
C. The [myproject:mydataset.mytable] table has too many partitions
D. Either the state or the city columns in the [myproject:mydataset.mytable]table have too many NULL values

Answer: A

NEW QUESTION # 272
You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:
* Each department should have access only to their data.
* Each department will have one or more leads who need to be able to create and update tables and provide them to their team.
* Each department has data analysts who need to be able to query but not modify data.
How should you set access to the data in BigQuery?

A. Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.
B. Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
C. Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
D. Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.

Answer: C

Explanation:
https://cloud.google.com/bigquery/docs/access-control-primitive-roles#dataset-primitive-roles

NEW QUESTION # 273
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

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

Answer: D

Explanation:
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: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 274
......

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