Google Professional-Data-Engineer Exam | Reliable Professional-Data-Engineer Exam Pattern - Ensure you Pass Professional-Data-Engineer: Google Certified Professional Data Engineer Exam Exam

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Google Professional-Data-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Certified Professional Data Engineer
Exam Number:	Professional-Data-Engineer
Available Languages:	English, Japanese
Passing Score:	Not officially published (estimated ~80%)
Exam Format:	Multiple-choice, Multiple-select
Exam Duration:	120 minutes
Exam Price:	$200 USD
Related Certifications:	Google Cloud Certified Professional Data Engineer
Certificate Validity Period:	2 years
Real Exam Qty:	50-60
Sample Questions:	Google Professional-Data-Engineer Sample Questions
Exam Way:	Online (remote proctored) or at a testing center (Kryterion)
Pre Condition:	No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-engineer

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Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

Google Certified Professional Data Engineer Exam Sample Questions (Q302-Q307):

NEW QUESTION # 302
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery.
Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

A. Partition table data by create_date, location_id and device_version
B. Cluster table data by create_date location_id and device_version
C. Partition table data by create_date cluster table data by tocation_id and device_version
D. Cluster table data by create_date, partition by location and device_version

Answer: B

NEW QUESTION # 303
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?

A. Google Cloud Storage
B. Google Cloud Datastore
C. Cloud Bigtable
D. Google BigQuery

Answer: C

Explanation:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using-gcp

NEW QUESTION # 304
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. The [myproject:mydataset.mytable] table has too many partitions
B. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many
NULL values
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 # 305
You are part of a healthcare organization where data is organized and managed by respective data owners in various storage services. As a result of this decentralized ecosystem, discovering and managing data has become difficult You need to quickly identify and implement a cost-optimized solution to assist your organization with the following
* Data management and discovery
* Data lineage tracking
* Data quality validation
How should you build the solution?

A. Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.
B. Use Dataplex to manage data, track data lineage, and perform data quality validation.
C. Use BigLake to convert the current solution into a data lake architecture.
D. Use BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation.

Answer: B

Explanation:
Dataplex is a Google Cloud service that provides a unified data fabric for data lakes and data warehouses. It enables data governance, management, and discovery across multiple data domains, zones, and assets.
Dataplex also supports data lineage tracking, which shows the origin and transformation of data over time.
Dataplex also integrates with Dataprep, a data preparation and quality tool that allows users to clean, enrich, and transform data using a visual interface. Dataprep can also monitor data quality and detect anomalies using machine learning. Therefore, Dataplex is the most suitable solution for the given scenario, as it meets all the requirements of data management and discovery, data lineage tracking, and data quality validation. References:
Dataplex overview
Automate data governance, extend your data fabric with Dataplex-BigLake integration Dataprep documentation

NEW QUESTION # 306
Which methods can be used to reduce the number of rows processed by BigQuery?

A. Putting data in partitions; using the LIMIT clause
B. Splitting tables into multiple tables; using the LIMIT clause
C. Splitting tables into multiple tables; putting data in partitions
D. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause

Answer: C

Explanation:
If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day. If you use the LIMIT clause, BigQuery will still process the entire table.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 307
......

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