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Preparation Process

To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.

Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer certification exam offers a quick way to validate skills in the market. By doing this they can upgrade their skill set and knowledge and become a certified member of the Google Certified Professional Data Engineer Exam Professional-Data-Engineer exam. There are several benefits of Professional-Data-Engineer Certification that can enjoy a successful candidate for the rest of their life. Professional-Data-Engineer also offers valid dumps book and valid dumps free download, with 365 days free updates.

Google Professional-Data-Engineer Certification is a highly sought-after certification that is designed for data professionals who are interested in demonstrating their proficiency in designing, building, and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for data engineers, data analysts, and data scientists who have experience working with data processing systems and are looking to advance their careers.

Google Certified Professional Data Engineer Exam Sample Questions (Q121-Q126):

NEW QUESTION # 121
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a numeric column from a feature cross of latitude and longitude.
B. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.
C. Provide latitude and longitude as input vectors to your neural net.
D. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.

Answer: A

Explanation:
Explanation
Reference https://cloud.google.com/bigquery/docs/gis-data

NEW QUESTION # 122
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. Retry the query every second until it comes back online to minimize staleness of data.
B. Reduce the query frequency to once every hour until the database comes back online.
C. Retry the query with exponential backoff, up to a cap of 15 minutes.
D. Issue a command to restart the database servers.

Answer: C

Explanation:
Explanation/Reference:

NEW QUESTION # 123
Which Java SDK class can you use to run your Dataflow programs locally?

A. LocalRunner
B. MachineRunner
C. DirectPipelineRunner
D. LocalPipelineRunner

Answer: C

Explanation:
Explanation
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION # 124
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

A. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
B. Use Cloud ML Engine for training existing Spark ML models
C. Rewrite your models on TensorFlow, and start using Cloud ML Engine
D. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

Answer: A

Explanation:
https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml

NEW QUESTION # 125
You have a variety of files in Cloud Storage that your data science team wants to use in their models Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?

A. Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
B. Create an external table in BigQuery and use SQL to transform the data as necessary Provide the data science team access to the external tables to explore the raw data.
C. Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.
D. Load the data into BigQuery and use SQL to transform the data as necessary Provide the data science team access to staging tables to explore the raw data.

Answer: A

Explanation:
Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure. The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep. Reference:
Dataprep overview
Dataprep features
Dataprep and BigQuery integration

NEW QUESTION # 126
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