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Google certification Professional-Data-Engineer exam targeted training

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

Choice of infrastructure
Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
Hybrid cloud and edge computing
Data modeling
Use of distributed systems
Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
Tradeoffs involving latency, throughput, transactions
Online (interactive) vs. batch predictions
Schema design
Designing data processing systems
Distributed systems
System availability and fault tolerance
Job automation and orchestration (e.g., Cloud Composer)
Capacity planning

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Google Professional-Data-Engineer Certification Exam is an online exam that tests the candidate's knowledge and skills in various areas such as data engineering, data analysis, machine learning, and big data processing. Professional-Data-Engineer exam consists of multiple-choice questions and requires a thorough understanding of the Google Cloud Platform, its services, and its features. Candidates need to demonstrate their proficiency in designing and implementing data processing systems that meet business requirements.

Google Certified Professional Data Engineer Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
You are working on a sensitive project involving private user dat
a. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

A. Grant the consultant the Cloud Dataflow Developer role on the project.
B. Grant the consultant the Viewer role on the project.
C. Create a service account and allow the consultant to log on with it.
D. Create an anonymized sample of the data for the consultant to work with in a different project.

Answer: C

NEW QUESTION # 118
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

A. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
B. Increase the cluster size with more non-preemptible workers.
C. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
D. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Answer: A

Explanation:
Explanation
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

NEW QUESTION # 119
Cloud Bigtable is a recommended option for storing very large amounts of
____________________________?

A. single-keyed data with very high latency
B. multi-keyed data with very low latency
C. multi-keyed data with very high latency
D. single-keyed data with very low latency

Answer: D

Explanation:
Explanation
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 120
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. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
B. The [myproject:mydataset.mytable] table has too many partitions
C. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
D. Users are running too many concurrent queries in the system

Answer: D

NEW QUESTION # 121
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 D
B. Option B.
C. Option C
D. Option A

Answer: D

NEW QUESTION # 122
......

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