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The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.
Google Professional-Data-Engineer Exam is designed to test an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, developers, and other IT professionals who are responsible for designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer exam covers a broad range of topics, including data processing, data warehousing, data analysis, and machine learning.
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Google Professional-Data-Engineer certification exam is a rigorous and comprehensive exam that requires individuals to have a deep understanding of data engineering technologies and concepts. Professional-Data-Engineer exam consists of multiple choice and scenario-based questions that assess an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer Exam is timed and individuals have a limited amount of time to complete the exam. To pass the exam, individuals must score 70% or higher.
NEW QUESTION # 291
You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?
Answer: D
NEW QUESTION # 292
Which of these statements about BigQuery caching is true?
Answer: B
Explanation:
When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table.
NEW QUESTION # 293
You are building a Dataflow pipeline to ingest customer feedback. Before loading to your data warehouse, you must validate email addresses and enrich unstructured comment strings with a generative AI sentiment classification. Invalid records need to be routed for manual review. How should you implement this pipeline?
Answer: B
Explanation:
This scenario requires a sophisticated streaming or batch ETL pipeline involving validation, AI enrichment, and branching logic. Apache Beam (Dataflow) is the standard tool for this on Google Cloud.
* Validation via ParDo: A ParDo (Parallel Do) transform is the fundamental way to perform element- wise logic in Dataflow. It can be used to run regex or validation logic on email strings for every record in the stream.
* Enrichment via RunInference: For integrating Generative AI or machine learning models into a Dataflow pipeline, the RunInference transform is the Google-recommended approach. It manages model loading and optimization (batching requests) to services like Vertex AI or local models, allowing for efficient sentiment classification during the "flight" of the data.
* Routing via Side Outputs: This is a key feature of Apache Beam. While a transform usually produces one main output, Side Outputs allow a single ParDo to emit data to multiple "p-collections." One collection can contain valid records destined for the data warehouse, while another contains invalid records routed to a "dead-letter" bucket or table for manual review.
* Correcting other options:
* B & C: These are "post-processing" approaches. Moving invalid data into a warehouse or a secondary service after the load increases complexity and cost, and violates the requirement to validate and enrich before loading.
* D: Relying on the source system for validation is often impossible in real-world data engineering where you don't control the source, and using BigQuery ML after the fact doesn't address the requirement of routing invalid records within the pipeline.
Reference: Google Cloud Documentation on Dataflow and Apache Beam:
"Side outputs are a powerful feature of the Beam model that allow you to produce multiple output PCollections from a single ParDo. This is useful for routing data to different destinations based on certain criteria, such as sending malformed data to a dead-letter queue." (Source: Apache Beam Programming Guide - Additional Outputs)
"The RunInference transform lets you perform internal and external model inference within your pipeline...
It handles the complexities of using machine learning models in a distributed data processing system." (Source: Dataflow ML - Use RunInference)
NEW QUESTION # 294
When you design a Google Cloud Bigtable schema it is recommended that you _________.
Answer: D
Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference:
https://cloud.google.com/bigtable/docs/schema-design#row-keys
NEW QUESTION # 295
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The
pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the
pipelines?
Answer: D
NEW QUESTION # 296
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