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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Google Cloud Associate Cloud Engineer Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst |
| Available Languages: | Japanese, English |
| Exam Format: | Multiple select, Multiple choice |
| Exam Price: | USD 200 (plus tax where applicable) |
| Exam Duration: | 120 minutes |
| Passing Score: | 700 / 1000 |
| Real Exam Qty: | 40 - 50 |
| Recommended Training: | Google Cloud Professional Data Engineer Learning Path Official Exam Guide |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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Google Professional-Data-Engineer Certification Exam is a comprehensive test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that assess the candidates' knowledge of various Google Cloud Platform services, including BigQuery, Cloud Spanner, Cloud Dataflow, and Cloud Dataproc. Additionally, the exam evaluates the candidates' ability to design data processing systems that are scalable, reliable, and secure.
NEW QUESTION # 210
Why do you need to split a machine learning dataset into training data and test data?
Answer: A
Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference:
https://machinelearningmastery.com/a-simple-intuition-for-overfitting/
NEW QUESTION # 211
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?
Answer: A,D
Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes.
[https://cloud.google.com/bigtable/docs/schema-design]
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti
NEW QUESTION # 212
You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?
Answer: A
NEW QUESTION # 213
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: C
Explanation:
When Dataflow fuses multiple transformations into a single stage (step), it can make it harder to pinpoint which specific part of that fused stage is causing a bottleneck because internal metrics for individual ParDos within the fused stage might not be as distinct.
* Reshuffle Operation (Option D):Inserting a Reshuffle (or GroupByKey followed by ungrouping, which forces a shuffle) operation between logical processing steps in your Beam pipeline prevents Dataflow from fusing those steps. A shuffle operation acts as a barrier to fusion. This materializes the intermediate PCollection and forces data to be redistributed across workers.
* Benefit for Debugging:By breaking the fusion, the Dataflow monitoring UI will display distinct steps for the operations before and after the Reshuffle. This allows you to observe metrics like processing time, throughput, and watermarks for each now-separated step, making it much easier to identify which part of your original fused logic is the bottleneck.
Let's analyze why other options are less effective for this specific problem of afused step:
* A (Verify service account permissions):While important for overall pipeline health, permission issues usually result in outright failures or errors in logs, not typically a slowdown within a successfully running (albeit slow) fused step.
* B (Insert output sinks):Adding actual output sinks (like writing to Pub/Sub or GCS) after each key step would also break fusion and allow you to measure throughput. However, it's a more heavyweight approach than Reshuffle. It introduces I/O overhead and requires setting up and managing these temporary sinks. Reshuffle is a lighter-weight way to achieve the same goal of breaking fusion for diagnostic purposes within the pipeline itself.
* C (Log debug information):Logging can be helpful, but if the entire fused step is slow, logs might not easily distinguish which internal operation is the culprit without very careful and verbose logging.
Analyzing potentially massive volumes of logs for performance bottlenecks can be less direct than observing stage metrics in the Dataflow UI once fusion is broken.
Using Reshuffle is a standard technique recommended by Google Cloud for debugging performance issues in fused Dataflow stages.
Reference:
Google Cloud Documentation: Dataflow > Troubleshooting Dataflow pipelines > Common Dataflow errors and troubleshooting steps > Pipeline is slow or stuck. "Break transform fusion: Certain transforms in your pipeline might be fused together into a single stage for optimization. If a particular fused stage is causing a bottleneck, you can temporarily add Reshuffle transforms between the fused transforms to break them into smaller, separate stages. This allows you to get more visibility into the performance of each individual transform and isolate the bottleneck." Apache Beam Documentation: Programming Guide > Pipeline I/O > Reshuffle."Reshuffle can be used to prevent fusion, and ensure that data is materialized and redistributed." (While the primary purpose of Reshuffle is often related to data distribution and freshness, a side effect and common use case is to break fusion for monitoring and debugging).
NEW QUESTION # 214
Which of the following statements is NOT true regarding Bigtable access roles?
Answer: A
Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances.
Read from and write to any table within the project, and manage instances.
Reference: https://cloud.google.com/bigtable/docs/access-control
NEW QUESTION # 215
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