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Professional-Data-Engineer Exam Sample Online & Professional-Data-Engineer Practice Exams Free

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Google Professional-Data-Engineer Certification is an excellent way for data processing professionals to demonstrate their knowledge and skills in designing and building data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification can help individuals advance their careers and open up new opportunities in the field of data engineering.

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To be eligible for the Google Professional-Data-Engineer Exam, candidates must have experience in data engineering, data analytics, and data warehousing. They must also have experience in designing and implementing solutions using Google Cloud Platform's data processing technologies, such as Cloud Dataflow, BigQuery, and Cloud Dataproc. Furthermore, candidates must have excellent knowledge of SQL, Python, and Java programming languages, as well as experience in data modeling and data visualization.

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 (Q177-Q182):

NEW QUESTION # 177
You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

A. Use watermarks to define the expected data arrival window Allow late data as it arrives.
B. Change your windowing function to session windows to define your windows based on certain activity.
C. Change your windowing function to tumbling windows to avoid overlapping window periods.
D. Expand your hopping window so that the late data has more time to arrive within the grouping.

Answer: A

Explanation:
Watermarks are a way of tracking the progress of time in a streaming pipeline. They are used to determine when a window can be closed and the results emitted. Watermarks can be either event-time based or processing-time based. Event-time watermarks track the progress of time based on the timestamps of the data elements, while processing-time watermarks track the progress of time based on the system clock. Event-time watermarks are more accurate, but they require the data source to provide reliable timestamps. Processing-time watermarks are simpler, but they can be affected by system delays or backlogs.
By using watermarks, you can define the expected data arrival window for each windowing function. You can also specify how to handle late data, which is data that arrives after the watermark has passed. You can either discard late data, or allow late data and update the results as new data arrives. Allowing late data requires you to use triggers to control when the results are emitted.
In this case, using watermarks and allowing late data is the best solution to capture the late data in the appropriate window. Changing the windowing function to session windows or tumbling windows will not solve the problem of late data, as they still rely on watermarks to determine when to close the windows. Expanding the hopping window might reduce the amount of late data, but it will also change the semantics of the windowing function and the results.
Reference:
Streaming pipelines | Cloud Dataflow | Google Cloud
Windowing | Apache Beam

NEW QUESTION # 178
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. Dimensionality Reduction
B. Dropout Methods
C. Threading
D. Serialization

Answer: B

Explanation:
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877

NEW QUESTION # 179
You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

A. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
B. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.
C. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
D. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

Answer: D

NEW QUESTION # 180
You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non- public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?

A. Grant the Project Owner role to a service account, and run the job with it
B. Restrict the Google Cloud Storage bucket so only you can see the files
C. Use a service account with the ability to read the batch files and to write to BigQuery
D. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery

Answer: A

NEW QUESTION # 181
You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

A. Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.
B. Adopt multi-regional Cloud Storage buckets in your architecture.
C. Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.
D. Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.

Answer: B

Explanation:
To ensure resilience and minimize the recovery point objective (RPO) with no impact on applications, using a dual-region bucket with turbo replication is the best approach. Here's why option D is the best choice:
* Dual-Region Buckets:
* Dual-region buckets store data redundantly across two distinct geographic regions, providing high availability and durability.
* This setup ensures that data remains available even if one region experiences a failure.
* Turbo Replication:
* Turbo replication ensures that data is replicated between the two regions within 15 minutes, aligning with the requirement to minimize the recovery point objective (RPO).
* This feature provides near real-time replication, significantly reducing the risk of data loss.
* No Impact on Applications:
* Applications continue to access the dual-region bucket without any changes, ensuring seamless operation even during a regional failure.
* The dual-region setup transparently handles failover, providing uninterrupted access to data.
Steps to Implement:
* Create a Dual-Region Bucket:
* Create a dual-region Cloud Storage bucket in the Google Cloud Console, selecting appropriate regions (e.g., us-central1 and us-east1).
* Enable Turbo Replication:
* Enable turbo replication to ensure rapid data replication between the selected regions.
* Configure Applications:
* Ensure that applications read and write to the dual-region bucket, benefiting from its high availability and durability.
* Test Failover:
* Simulate a regional failure to verify that the dual-region bucket and turbo replication meet the required RPO and ensure data resilience.
Reference Links:
* Google Cloud Storage Dual-Region
* Turbo Replication in Google Cloud Storage

NEW QUESTION # 182
......

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