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NEW QUESTION # 185
Which of the following statements about Legacy SQL and Standard SQL is not true?
Answer: A
Explanation:
Explanation
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released.
In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql
NEW QUESTION # 186
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average
200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?
Answer: C
NEW QUESTION # 187
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: A
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 # 188
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and- shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery.
You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
Answer: A
NEW QUESTION # 189
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?
Answer: C
NEW QUESTION # 190
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