Reliable Google - Professional-Data-Engineer Questions Exam
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Google Professional-Data-Engineer certification exam is a comprehensive exam that requires detailed knowledge of data engineering concepts and technologies. It is designed to assess the candidate's ability to apply this knowledge to real-world scenarios, and to design and implement solutions that meet the needs of a wide range of users. Professional-Data-Engineer exam is intended for professionals who have experience working with data engineering technologies and who are looking to advance their careers in this field.
Google Professional-Data-Engineer certification exam is designed to assess an individual's ability to design, build, and maintain data processing systems using Google Cloud Platform technologies. Google Certified Professional Data Engineer Exam certification exam is intended for professionals who have experience working with data technologies and are looking to enhance their skills and knowledge in cloud-based data engineering. Professional-Data-Engineer Exam covers a wide range of topics such as data storage, data processing, data analysis, machine learning, and data visualization.
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The Google Professional-Data-Engineer Exam is intended for data engineers, data analysts, and other professionals who work with large data sets and need to design and implement scalable, reliable, and efficient data processing systems. It is also suitable for IT professionals who are responsible for managing data pipelines and ensuring the security, privacy, and compliance of data on Google Cloud Platform.
Google Certified Professional Data Engineer Exam Sample Questions (Q275-Q280):
NEW QUESTION # 275
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?
Answer: A
Explanation:
Explanation
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use
100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance
NEW QUESTION # 276
You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?
Answer: A
Explanation:
Google recommends that enterprises use Transfer Appliance in cases where it would take them over a week to upload data to the cloud via the internet, or when an enterprise needs to migrate over 60 TB of data.
NEW QUESTION # 277
Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?
Answer: B
Explanation:
For example, if you plan to store extensive historical data for a large number of remote- sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage-reads would be much more frequent in this case, and reads are much slower with HDD storage.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd
NEW QUESTION # 278
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?
Answer: C
Explanation:
Fusion is an optimization technique that Dataflow applies to merge multiple transforms into a single stage. This reduces the overhead of shuffling data between stages, but it can also limit the parallelism and scalability of the pipeline. By introducing a Reshuffle step, you can force Dataflow to split the pipeline into multiple stages, which can increase the number of workers that can process the data in parallel. Reshuffle also adds randomness to the data distribution, which can help balance the workload across workers and avoid hot keys or skewed data. Reference:
1: Streaming pipelines
2: Batch vs Streaming Performance in Google Cloud Dataflow
3: Deploy Dataflow pipelines
4: How Distributed Shuffle improves scalability and performance in Cloud Dataflow pipelines
5: Managing costs for Dataflow batch and streaming data processing
NEW QUESTION # 279
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:
What is the most likely cause of the delay for this query?
Answer: B
NEW QUESTION # 280
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