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Google Professional-Data-Engineer Certification Exam is designed for professionals who want to demonstrate their skills in designing and building data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is ideal for data engineers, data analysts, and database administrators who work with big data and want to enhance their skills. Professional-Data-Engineer exam covers a broad range of topics, including data processing, data storage, data analysis, and machine learning.

Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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Google Professional-Data-Engineer certification exam is a tough test that measures the competency of candidates in a variety of areas related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that cover topics such as data processing, data storage, data analysis, data visualization, and machine learning. Professional-Data-Engineer Exam also includes hands-on tasks that require candidates to demonstrate their ability to use Google Cloud Platform tools and services to solve real-world data engineering problems.

Google Certified Professional Data Engineer Exam Sample Questions (Q211-Q216):

NEW QUESTION # 211
You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests dat a. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

A. Create a BigQuery reservation for the dataset.
B. Create a BigQuery reservation for the job.
C. Create a BigQuery reservation for the service account running the job.
D. Create a BigQuery reservation for the project.

Answer: D

Explanation:
https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas.
Here's why creating a BigQuery reservation for the project is the most suitable solution:
Project-Level Reservation: BigQuery reservations are applied at the project level. This means that the reserved slots (processing capacity) are shared across all jobs and queries running within that project. Since your CDC process is a significant contributor to your BigQuery usage, reserving slots for the entire project ensures that your CDC process always has access to the necessary resources, regardless of other activities in the project.
Predictable Cost Model: Reservations provide a fixed, predictable cost model. Instead of paying the on-demand price for each query, you pay a fixed monthly fee for the reserved slots. This eliminates the variability of costs associated with on-demand billing, making it easier to budget and forecast your BigQuery expenses.
BigQuery Monitoring: You can use BigQuery Monitoring to analyze the historical usage patterns of your CDC process and other queries within your project. This information helps you determine the appropriate amount of slots to reserve, ensuring that you have enough capacity to handle your workload while optimizing costs.
Why other options are not suitable:
A . Create a BigQuery reservation for the job: BigQuery does not support reservations at the individual job level. Reservations are applied at the project or assignment level.
B . Create a BigQuery reservation for the service account running the job: While you can create reservations for assignments (groups of users or service accounts), it's less efficient than a project-level reservation in this scenario. A project-level reservation covers all jobs within the project, regardless of the service account used.
C . Create a BigQuery reservation for the dataset: BigQuery does not support reservations at the dataset level.
By creating a BigQuery reservation for your project based on your utilization analysis, you can achieve a predictable cost model while ensuring that your CDC process and other queries have the necessary resources to run smoothly.

NEW QUESTION # 212
You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the data. What should you do?

A. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
2. Enable turbo replication.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us- south1 region.
4. In case of a regional failure, redeploy your Dataproc cluster to the us-south1 region and continue reading from the same bucket.
B. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
2. Enable turbo replication.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region.
4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.
C. 1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us- south1 region.
2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region.
4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead.
D. 1. Create a Cloud Storage bucket in the US multi-region.
2. Run the Dataproc cluster in a zone in the us-central1 region, reading data from the US multi- region bucket.
3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.

Answer: B

NEW QUESTION # 213
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
Configure the job to use non-default Compute Engine machine types when needed.
B. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
C. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
D. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Answer: B

Explanation:
Dataflow is good with autoscaling and stackdriver to monitor CPU and Storage.

NEW QUESTION # 214
Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

A. 1 Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own lake's data assets.
B. 1. Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all zones' data assets.
C. 1 Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all lakes' data assets.
D. 1 Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each to the BigQuory datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own zone's data assets.

Answer: D

Explanation:
To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:
Data Mesh Architecture:
Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.
Dataplex Lakes and Zones:
Lakes in Dataplex are logical containers for managing data at scale, and zones are subdivisions within lakes for organizing data based on domains, teams, or other criteria.
Domain and Team Management:
By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.
This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.
Implementation Steps:
Create Lakes and Zones:
Create separate lakes in Dataplex for each team (analytics and data science).
Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).
Attach BigQuery Datasets:
Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.
Decentralized Management:
Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.
Reference:
Dataplex Documentation
BigQuery Documentation
Data Mesh Principles

NEW QUESTION # 215
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

A. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
B. Create a table in BigQuery, and append the new samples for CPU and memory to the table
C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
D. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second

Answer: C

Explanation:
https://cloud.google.com/bigtable/docs/schema-design-time-series

NEW QUESTION # 216
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