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Google Professional-Data-Engineer certification exam is a rigorous and challenging test of data engineering skills and knowledge. Candidates who pass the exam will have demonstrated their ability to design and implement highly scalable and reliable data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is highly respected in the industry and can be a valuable asset for data engineers seeking to advance their careers.

Google Professional-Data-Engineer certification exam is designed to validate the skills and knowledge of individuals working in the field of data engineering. Google Certified Professional Data Engineer Exam certification is intended for those professionals who have expertise in designing, building, and maintaining data processing systems using Google Cloud Platform services. Professional-Data-Engineer Exam evaluates the candidates' ability to design, implement, and manage data processing systems, as well as their understanding of data analysis and machine learning concepts.

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Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

Google Certified Professional Data Engineer Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
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 closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to 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. Rewrite your models on TensorFlow, and start using Cloud ML Engine
B. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
C. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
D. Use Cloud ML Engine for training existing Spark ML models

Answer: D

Explanation:
Explanation

NEW QUESTION # 13
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline.
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
B. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2 Create a separate DAG for each table that needs to go through the pipeline
3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
C. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators
2 Create a separate DAG for each table that needs to go through the pipeline
3 Schedule the DAGs to run hourly
D. 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators
2 Use a single shared DAG for all tables that need to go through the pipeline
3 Schedule the DAG to run hourly

Answer: B

Explanation:
This option is the most efficient and maintainable workflow for your use case, as it allows you to process each table independently and trigger the DAGs only when new data arrives in the Cloud Storage bucket. By using the Dataproc and BigQuery operators, you can easily orchestrate the load and transformation jobs for each table, and leverage the scalability and performance of these services12. By creating a separate DAG for each table, you can customize the transformation logic and parameters for each table, and avoid the complexity and overhead of a single shared DAG3. By using a Cloud Storage object trigger, you can launch a Cloud Function that triggers the DAG for the corresponding table, ensuring that the data is processed as soon as possible and reducing the idle time and cost of running the DAGs on a fixed schedule4 .
Option A is not efficient, as it runs the DAG hourly regardless of the data arrival, and it uses a single shared DAG for all tables, which makes it harder to maintain and debug. Option C is also not efficient, as it runs the DAGs hourly and does not leverage the Cloud Storage object trigger. Option D is not maintainable, as it uses a single shared DAG for all tables, and it does not use the Cloud Storage operator, which can simplify the data ingestion from the bucket. References:
* 1: Dataproc Operator | Cloud Composer | Google Cloud
* 2: BigQuery Operator | Cloud Composer | Google Cloud
* 3: Choose Workflows or Cloud Composer for service orchestration | Workflows | Google Cloud
* 4: Cloud Storage Object Trigger | Cloud Functions Documentation | Google Cloud
* [5]: Triggering DAGs | Cloud Composer | Google Cloud
* [6]: Cloud Storage Operator | Cloud Composer | Google Cloud

NEW QUESTION # 14
You work for a large real estate firm and are preparing 6 TB of home sales data to be used for machine learning. You will use SQL to transform the data and use BigQuery ML to create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

A. Use a BigQuery view to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.
B. Preprocess all data using Dataflow. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any further transformations on the input data.
C. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.
D. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. Before requesting predictions, use a saved query to transform your raw input data, and then use ML.EVALUATE.

Answer: D

NEW QUESTION # 15
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

A. 1 TB
B. 500 TB
C. 500 GB
D. 1 GB

Answer: A

Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It is not a good solution for less than 1 TB of data.
Reference: https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options

NEW QUESTION # 16
Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility.
The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings.
They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?

A. Migrate the workloads to Dataproc plus Cloud Storage; modernize later.
B. Migrate the workloads to Dataproc plus HDFS; modernize later.
C. Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.
D. Modernize the Spark workload for Dataflow and the Hive workload for BigQuery.

Answer: D

NEW QUESTION # 17
......

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Valid Professional-Data-Engineer Test Book - Professional-Data-Engineer Free Exam Dumps

Quiz 2025 Trustable Google Professional-Data-Engineer: Valid Google Certified Professional Data Engineer Exam Test Book

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Google Certified Professional Data Engineer Exam Sample Questions (Q12-Q17):

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