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100% Real Google Cloud Certified - Professional Data Engineer Practice Test

June 09,2019

Killtest today comes with 100% Real Google Cloud Certified - Professional Data Engineer Practice Test to help you pass Google Certified Professional – Data Engineer exam. The most updated Professional Data Engineer Practice Test contains 100% real exam questions and answers, which will be your great online resource for passing Professional Data Engineer exam. Professional Data Engineer exam objectively measures the ability of an individual to demonstrate the critical job skills for the role. In the fast forwarding world, passing Professional Data Engineer exam has become essential to keep pace with IT world. When mention Professional Data Engineer exam, things which comes first in mind is how to pass Professional Data Engineer exam smoothly. Killtest provides you 100% Real Google Cloud Certified - Professional Data Engineer Practice Test that covers every topic of the Professional-Data-Engineer exam syllabus for passing with 100% guarantee.


Why Get Professional Data Engineer Certification?


In the modern world, the work of Data Engineers is extremely technical. Data Engineers should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. Also, Data Engineers be able to leverage, deploy, and continuously train pre-existing machine learning models. Nowadays, the demand for skilled Data Engineers is projected to rapidly grow. More and more businesses and organizations require a robust Data Architecture for storing and accessing data. Data engineers are needed when an organization expands into using Data Science. Consequently, there has been a recent run on Data Engineers. 


Professional Data Engineer exam enables data-driven decision making by collecting, transforming, and publishing data. This exam demonstrates your proficiency at designing and building data-processing systems, and your skill at creating machine-learning models on the Google Cloud Platform. Companies around the world are hiring data engineers to develop their data infrastructure. In particular, look for positions at software corporations, computer manufacturers, and computer system design companies. With the Google data engineer certification, you will be able to validate your skills in the above-described interests. Acquiring a Google Cloud Certified - Professional Data Engineer certification is not a difficult process but has a meaningful impact on your career and job in the IT industry.  


Main Benefits of getting Professional Data Engineer Certification:

 Enhanced knowledge and understanding of technology

● You have an extra edge over other candidates

● It acts as proof of your continuous learning

● If you are familiar with Professional Data Engineer then also you need a certificate to show in the job

● Recognize you as a Google certified data engineer professional globally

● It increases your chances of getting better opportunities and higher salary


What does a Professional Data Engineer do?


The data engineer is chiefly in charge of designing, building, testing, and maintaining data management systems. Google Cloud Certification is one of the most sought-after IT certifications all over the world. It has become really important for any Data Engineer to get a Google Certification to progress in the IT industry. 


The Professional Data Engineer exam assesses your ability to:

● Design data processing systems

● Build and Operationalize Data Processing Systems

● Operationalize Machine Learning Models

● Ensure Solution Quality


What is the overview of Google Data Engineer certification exam?


  Google Cloud Certified - Professional Data Engineer Overview

Certification Name

Google Certified Professional – Data Engineer 


Basic Knowledge of Database 

 Exam Duration

2 hours 

 Registration Fee

USD $200 

Languages Available 

 English, Japanese, Spanish, Portuguese


2 years 

Exam Format 

Multiple Choice Questions 

Why choose 100% Real Google Cloud Certified - Professional Data Engineer Practice Test?

There are many websites who claim to have the best Google Cloud Certified - Professional Data Engineer Practice Test but somehow they are not much effective for the preparation as they lack the updated questions. Killtest updated Professional Data Engineer Practice Test on June 9, 2019, which is full of real exam questions and answers. From Killtest, you can get all the related and updated Professional Data Engineer questions on the flick of fingers. With the help of Killtest 100% Real Google Cloud Certified - Professional Data Engineer Practice Test, you can prepare for the Professional Data Engineer exam within the shortest possible span and will be able to score high scoring grades.

Try Free Professional Data Engineer Practice Test Questions Online

Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data.
However, when tested against new data, it performs poorly.
What method can you employ to address this?
A. Threading
B. Serialization
C. Dropout Methods
D. Dimensionality Reduction
Answer: C

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
A. Continuously retrain the model on just the new data.
B. Continuously retrain the model on a combination of existing data and the new data.
C. Train on the existing data while using the new data as your test set.
D. Train on the new data while using the existing data as your test set.
Answer: D

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources.
How should you adjust the database design?
A. Add capacity (memory and disk space) to the database server by the order of 200.
B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
D. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
Answer: B

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

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted.
How should you build this pipeline?
A. Use federated data sources, and check data in the SQL query.
B. Enable BigQuery monitoring in Google Stackdriver and create an alert.
C. Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.
D. Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.
Answer: D

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users.
How should you design the frontend to respond to a database failure?
A. Issue a command to restart the database servers.
B. Retry the query with exponential backoff, up to a cap of 15 minutes.
C. Retry the query every second until it comes back online to minimize staleness of data.
D. Reduce the query frequency to once every hour until the database comes back online.
Answer: B

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine.
Which learning algorithm should you use?
A. Linear regression
B. Logistic classification
C. Recurrent neural network
D. Feedforward neural network
Answer: A

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data.
Which query type should you use?
A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
B. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
D. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.
Answer: D

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery.
Which three approaches can you take? (Choose three.)
A. Disable writes to certain tables.
B. Restrict access to tables by role.
C. Ensure that the data is encrypted at all times.
D. Restrict BigQuery API access to approved users.
E. Segregate data across multiple tables or databases.
F. Use Google Stackdriver Audit Logging to determine policy violations.
Answer: B,D,F

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's data. You want to ensure appropriate access to the data.
Which three steps should you take? (Choose three.)
A. Load data into different partitions.
B. Load data into a different dataset for each client.
C. Put each client’s BigQuery dataset into a different table.
D. Restrict a client’s dataset to approved users.
E. Only allow a service account to access the datasets.
F. Use the appropriate identity and access management (IAM) roles for each client’s users.
Answer: B,D,F

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