CertyIQ - Google - Prof Data Eng - pt 6

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?

• Use Vertex AI for training existing Spark ML models

• Rewrite your models on TensorFlow, and start using Vertex AI

• Use Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

• Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

• BigQuery

• Cloud Bigtable

• Cloud Datastore

• Cloud SQL for PostgreSQL

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

• Consume the stream of data in Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

• Consume the stream of data in Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

• Use Kafka Connect to link your Kafka message queue to Pub/Sub. Use a Dataflow template to write your messages from Pub/Sub to Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Bigtable in the last hour. If that number falls below 4000, send an alert.

• Use Kafka Connect to link your Kafka message queue to Pub/Sub. Use a Dataflow template to write your messages from Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

• Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.

• Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.

• Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

• Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

• Apache Kafka

• Cloud Storage

• Dataflow

• Firebase Cloud Messaging

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• Deploy a Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

• Deploy a Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

• Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances. Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://

• Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances. Store data in HDFS. Change references in scripts from hdfs:// to gs://

Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

• Perform hyperparameter tuning

• Train a classifier with deep neural networks, because neural networks would always beat SVMs

• Deploy the model and measure the real-world AUC; it's always higher because of generalization

• Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

You need to deploy additional dependencies to all nodes of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

• Deploy the Cloud SQL Proxy on the Cloud Dataproc master

• Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

• Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

• Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

• Cloud SQL

• Cloud Bigtable

• Cloud Spanner

• Cloud Datastore

You work for a mid-sized enterprise that needs to move its operational system transaction data from an onpremises database to GCP. The database is about 20 TB in size. Which database should you choose?

• Cloud SQL

• Cloud Bigtable

• Cloud Spanner

• Cloud Datastore