Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

• A.Threading
• B.Serialization
• C.Dropout Methods
• D.Dimensionality Reduction

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The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

• A.Cloud Dataflow connector
• B.DataFlow SDK
• C.BiqQuery API
• D.BigQuery Data Transfer Service

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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 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?

• A.Use Cloud ML Engine for training existing Spark ML models
• B.Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
• C.Rewrite your models on TensorFlow, and start using Cloud ML Engine
• D.Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

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You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

• A.Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
• B.Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
• C.Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
• D.Add capacity (memory and disk space) to the database server by the order of 200.

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You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie'the property 'actors'and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?


C: Set the following in your entity options: exclude_from_indexes = 'actors, tags' D: Set the following in your entity options: exclude_from_indexes = 'date_published'

• A.Option C
• B.Option B.
• C.Option D
• D.Option A

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