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Professional-Data-Engineer Exam Question & Exam Professional-Data-Engineer Questions Pdf

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Google Certified Professional Data Engineer Exam Sample Questions (Q195-Q200):

NEW QUESTION # 195
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

A. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.
B. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
C. Disable external IP addresses from worker VMs and enable Private Google Access.
D. Disable external IP addresses and establish a Private Service Connect endpoint IP address.

Answer: C

Explanation:
Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs. Reference:
Private Google Access overview
Using Private Google Access and Cloud NAT
Using custom containers with Dataflow

NEW QUESTION # 196
Which of the following is NOT true about Dataflow pipelines?

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

Answer: B

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs

NEW QUESTION # 197
The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

A. Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level
B. Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level
C. Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project
D. Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

Answer: B

NEW QUESTION # 198
When a Cloud Bigtable node fails, ____ is lost.

A. all data
B. no data
C. the time dimension
D. the last transaction

Answer: B

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node. Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node. Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 199
You need to analyze user clickstream data to personalize content recommendations. The data arrives continuously and needs to be processed with low latency, including transformations such as sessionization (grouping clicks by user within a time window) and aggregation of user activity. You need to identify a scalable solution to handle millions of events each second and be resilient to late-arriving data. What should you do?

A. Use Cloud Storage for ingestion, Dataproc with Apache Spark for batch processing, and BigQuery for storage and analytics.
B. Use Pub/Sub for ingestion, Dataflow with Apache Beam for processing, and BigQuery for storage and analytics.
C. Use Cloud Data Fusion for ingestion and transformation, and Cloud SQL for storage and analytics.
D. Use Firebase Realtime Database for ingestion and storage, and Cloud Run functions for processing and analytics.

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
This question requires a solution that excels at large-scale, stateful stream processing with sophisticated windowing and handling of out-of-order data.
Option C is the correct answer because this architecture is perfectly suited for the requirements.
Pub/Sub is the global, scalable ingestion service for continuous event data.
Dataflow, with the Apache Beam programming model, is specifically designed for complex stream processing. It has powerful, built-in support for different windowing strategies (including session windows for sessionization) and sophisticated triggers for handling late-arriving data. Its serverless nature ensures it scales to handle millions of events.
BigQuery is the ideal sink for the processed data, enabling large-scale analytics for the recommendation engine.
Option A is incorrect as Firebase and Cloud Run are more suited for application backends and are not designed for complex, stateful data processing pipelines at this scale.
Option B is incorrect because it describes a batch processing pattern. Using Cloud Storage for ingestion and Dataproc for batch processing would introduce high latency, failing the "low latency" requirement.
Option D is incorrect because Cloud Data Fusion is primarily a batch-oriented ETL/ELT tool, and Cloud SQL is not an analytical data warehouse capable of handling this scale of data for analytics.
Reference (Google Cloud Documentation Concepts):
This is another example of the canonical Pub/Sub -> Dataflow -> BigQuery streaming analytics pattern. The Apache Beam Programming Guide (which is the foundation for Dataflow) extensively covers concepts like Windowing (specifically SessionWindows) and Triggers for handling late data. These features are critical for accurately processing real-world event streams like clickstream data and are core strengths of Dataflow.

NEW QUESTION # 200
......

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