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Google Professional-Data-Engineer certification exam covers a broad range of topics, including data processing systems, data modeling, data analysis, data visualization, and machine learning. It requires a strong understanding of Google Cloud Platform products and services, such as BigQuery, Dataflow, Dataproc, and Pub/Sub. Professional-Data-Engineer Exam also tests the ability to design and implement solutions that are scalable, efficient, and secure.

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Professional-Data-Engineer Latest Dumps Questions, Question Professional-Data-Engineer Explanations

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Google Certified Professional Data Engineer Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

Answer: B

Explanation:
A Reshuffle operation is a way to force Dataflow to split the pipeline into multiple stages, which can help isolate the performance of each step and identify bottlenecks. By monitoring the execution details in the Dataflow console, you can see the time, CPU, memory, and disk usage of each stage, as well as the number of elements and bytes processed. This can help you diagnose where the pipeline is slowing down and optimize it accordingly. Reference:
1: Reshuffling your data
2: Monitoring pipeline performance using the Dataflow monitoring interface
3: Optimizing pipeline performance


NEW QUESTION # 171
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)

Answer: B,D


NEW QUESTION # 172
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
Decoupling producer from consumer
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
Near real-time SQL query
Maintain at least 2 years of historical data, which will be queried with SQ
Which pipeline should you use to meet these requirements?

Answer: C


NEW QUESTION # 173
You need to orchestrate a pipeline with several Google Cloud services: a batch Dataflow job, then a BigQuery query job followed by a Vertex AI batch prediction. The logic is sequential. You want a lightweight, serverless orchestration solution with minimal operational overhead. What service should you use?

Answer: B

Explanation:
When the requirement specifies a "lightweight" and "serverless" orchestration for Google Cloud APIs with
"minimal operational overhead," Cloud Workflows is the preferred choice over Cloud Composer.
* Lightweight and Serverless: Cloud Workflows is a fully managed, HTTP-based orchestration service that scales to zero and has no base cost. It is designed specifically to chain Google Cloud services together using YAML or JSON.
* Operational Overhead: Unlike Cloud Composer (which requires managing a Kubernetes-based environment and has a minimum running cost), Workflows is truly serverless with no infrastructure to manage.
* Service Integration: Workflows has built-in connectors for Dataflow, BigQuery, and Vertex AI, making it ideal for simple sequential logic.
* Correcting other options:
* B (Cloud Composer): While it can handle this logic, it is not "lightweight." It is better suited for complex data engineering pipelines with non-GCP dependencies.
* C (Compute Engine): This is not serverless and requires significant operational overhead to manage the VM and cron state.
* D (Dataproc/Oozie): This is a legacy Hadoop-based orchestration tool and is definitely not lightweight or serverless.
Reference: Google Cloud Documentation on Workflows:
"Workflows is a fully managed orchestration platform that executes services in an order that you define...
Workflows is serverless, scales to zero, and has no infrastructure to manage. It is ideal for orchestrating Google Cloud services like BigQuery, Dataflow, and Vertex AI with low latency." (Source: Workflows product overview)
"Use Workflows for low-latency, high-volume, and lightweight orchestration of Google Cloud services." (Source: Choose an orchestration service)


NEW QUESTION # 174
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?

Answer: D

Explanation:
A tall and narrow table has a small number of events per row, which could be just one event, whereas a short and wide table has a large number of events per row. As explained in a moment, tall and narrow tables are best suited for time-series data. For time series, you should generally use tall and narrow tables. This is for two reasons: Storing one event per row makes it easier to run queries against your data. Storing many events per row makes it more likely that the total row size will exceed the recommended maximum (see Rows can be big but are not infinite).
https://cloud.google.com/bigtable/docs/schema-design-time-series#patterns_for_row_key_design


NEW QUESTION # 175
......

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