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You can overcome this hurdle by selecting real Google Professional-Data-Engineer Exam Dumps that can help you ace the Professional-Data-Engineer test quickly on the maiden endeavor. If you aspire to earn the Google Professional-Data-Engineer Certification then obtaining trusted prep material is the most significant part of your Professional-Data-Engineer test preparation.

This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

Big Data Analytics with Cloud Al Platform Notebook
Performing Spark on Cloud Dataproc
Introduction to Processing Streaming Data
Prebuilt ML Models APIs for Unsaturated Data
Advanced BigQuery Performance and Functionality
Introduction to Data Engineering
Custom Model building Using SQL in BigQuery ML
Cloud Dataflow Streaming Features
Serverless Data Processing with Cloud Dataflow
Handling Data Pipelines with Cloud Composer and Cloud Data Fusion

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

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

Length of Examination: 120 minutes
Language: English (U.S.), Japanese, Spanish, and Portuguese
Passing score: 80%

Google Professional-Data-Engineer Certification Exam is designed to validate the skills and knowledge of professionals who work with data on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification demonstrates a candidate’s expertise in designing, building, and maintaining data processing systems, as well as their ability to leverage the power of Google Cloud Platform to solve complex business problems.

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Google Certified Professional Data Engineer Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

A. Refresh your browser tab showing the visualizations.
B. Clear your browser history for the past hour then reload the tab showing the virtualizations.
C. Disable caching in BigQuery by editing table details.
D. Disable caching by editing the report settings.

Answer: D

NEW QUESTION # 36
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_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Manually configure the index in your index config as follows:
B. Set the following in your entity options: exclude_from_indexes = 'actors, tags'
C. Set the following in your entity options: exclude_from_indexes = 'date_published'
D. Manually configure the index in your index config as follows:

Answer: A

NEW QUESTION # 37
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Split table into multiple tables; 2) Use a partitioned table
B. 1) Use a partitioned table; 2) Join tables into one table
C. 1) Join tables into one table; 2) Use nested repeated fields
D. 1) Use nested repeated fields; 2) Use a partitioned table

Answer: C

Explanation:
Explanation
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 38
An aerospace company uses a proprietary data format to store its night dat
a. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

A. Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used
B. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format
C. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format
D. Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

Answer: C

NEW QUESTION # 39
You work for an economic consulting firm that helps companies identify economic trends as they happen.
As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Load the data every 30 minutes into a new partitioned table in BigQuery.
B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Answer: A

NEW QUESTION # 40
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