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The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) certification is a valuable credential that every Google professional should earn it. The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) certification exam offers a great opportunity for beginners and experienced professionals to demonstrate their expertise. With the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) certification exam everyone can upgrade their skills and knowledge. There are other several benefits that the Google Professional-Data-Engineer exam holders can achieve after the success of the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) certification exam.

Google Professional-Data-Engineer Certification is highly valued in the industry. It demonstrates that the holder has the skills and knowledge to design and implement data solutions on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is especially relevant for those looking to work with Big Data, as Google Cloud Platform is one of the leading providers of Big Data solutions.

Google Certified Professional Data Engineer Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A. An hourly watermark
B. An event time trigger
C. A processing time trigger
D. The with Allowed Lateness method

Answer: C

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time ?the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers

NEW QUESTION # 98
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
B. Dataflow pipelines can be programmed in Java
C. Dataflow pipelines can consume data from other Google Cloud services
D. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Answer: D

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 99
Your company needs to ingest and transform streaming data from IoT devices and store it for analysis. The data is sensitive and requires encryption with your own key in transit and at rest. The volume of data is expected to fluctuate significantly throughout the day. You need to identify a solution that is managed and elastic. What should you do?

A. Publish data to Pub/Sub, process it with Dataflow and store it in Cloud SQL, selecting your key from Cloud HSM for each service.
B. Write data directly into Cloud Storage, process it with Dataproc, and store it in BigQuery, selecting a customer-managed encryption key (CMEK) for each service.
C. Write data directly into BigQuery by using the Storage Write API, and process it in BigQuery by using SQL functions, selecting a Google-managed encryption key for each service.
D. Publish data to Pub/Sub, process it with Dataflow and store it in BigQuery, selecting your key from Cloud KMS for each service.

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
This question describes a classic, scalable streaming analytics architecture on Google Cloud.
Option C is the correct answer as it combines the best-in-class managed services for each part of the pipeline.
Pub/Sub is a fully managed, highly scalable messaging service perfect for ingesting fluctuating volumes of streaming IoT data.
Dataflow is a fully managed, serverless service for stream and batch processing that automatically scales resources up and down to handle fluctuating data volumes.
BigQuery is a serverless, highly scalable data warehouse optimized for analytics.
Cloud KMS is the standard Google Cloud service for creating and managing your own cryptographic keys, which are then used to enable Customer-Managed Encryption Keys (CMEK) across services like Pub/Sub, Dataflow, and BigQuery, satisfying the security requirement.
Option A is incorrect because it specifies using a Google-managed encryption key, which violates the requirement for encryption with "your own key." Option B is incorrect because Cloud SQL is a relational database (OLTP), not an analytical data warehouse (OLAP), making it unsuitable for storing and analyzing large volumes of streaming data. BigQuery is the appropriate choice.
Option D is incorrect because Dataproc is a managed Hadoop/Spark service. While powerful, it is less
"managed" and serverless than Dataflow, as you still need to provision and manage clusters. For a fully elastic and managed solution, Dataflow is the preferred choice.
Reference (Google Cloud Documentation Concepts):
This architecture is a canonical pattern for streaming analytics on Google Cloud. The "Streaming analytics" solution guide frequently highlights the Pub/Sub -> Dataflow -> BigQuery pattern. Each of these services supports Customer-Managed Encryption Keys (CMEK) using keys from Cloud KMS, ensuring data is protected at rest with customer-controlled keys, which aligns with the principle of customer control over data security.

NEW QUESTION # 100
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat
a. Which Stackdriver alerts should you create?

A. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
D. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination

Answer: B

NEW QUESTION # 101
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
B. Introduce resource hierarchy to leverage access control policy inheritance.
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. Use Cloud Deployment Manager to automate access provision.
E. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

Answer: C,D

NEW QUESTION # 102
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