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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer Exam |
| Exam Number: | Professional Data Engineer |
| Exam Duration: | 120 minutes |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Real Exam Qty: | Approximately 50–60 multiple choice and multiple select questions |
| Related Certifications: | Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Associate Cloud Engineer |
| Certificate Validity Period: | 2 years |
| Exam Format: | multiple select, multiple choice, case study |
| Exam Price: | $200 USD |
| Available Languages: | French, English, Portuguese, Japanese, German, Spanish |
| Recommended Training: | Google Cloud Professional Data Engineer Course (Coursera) Google Cloud Data Engineering Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam or onsite test center (Kryterion Webassessor) |
| Pre Condition: | No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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Google Professional-Data-Engineer certification exam is a rigorous and challenging test of data engineering skills and knowledge. Candidates who pass the exam will have demonstrated their ability to design and implement highly scalable and reliable data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is highly respected in the industry and can be a valuable asset for data engineers seeking to advance their careers.
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NEW QUESTION # 316
Which of these is not a supported method of putting data into a partitioned table?
Answer: C
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
NEW QUESTION # 317
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: D
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 # 318
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings.
Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat
Answer: B
NEW QUESTION # 319
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time.
Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?
Answer: D
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series
NEW QUESTION # 320
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?
Answer: B
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/dataproc/
NEW QUESTION # 321
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