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NEW QUESTION 53
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform.
Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A. BigQueryB. Cloud BigtableC. Cloud SQLD. Cloud Datastore

Answer: C

 

NEW QUESTION 54
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?

A. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the clusterB. Create a Directed Acyclic Graph in Cloud ComposerC. Create a Cloud Dataproc Workflow TemplateD. Create an initialization action to execute the jobs

Answer: B

Explanation:
References:

 

NEW QUESTION 55
Scaling a Cloud Dataproc cluster typically involves ____.

A. deleting applications from unused nodes periodicallyB. increasing or decreasing the number of worker nodesC. increasing or decreasing the number of master nodesD. moving memory to run more applications on a single node

Answer: B

Explanation:
Explanation
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1) increase the number of workers to make a job run faster
2) decrease the number of workers to save money
3) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage Reference: https://cloud.google.com/dataproc/docs/concepts/scaling-clusters

 

NEW QUESTION 56
Your company built a TensorFlow neutral-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. Dropout MethodsB. SerializationC. ThreadingD. Dimensionality Reduction

Answer: A

Explanation:
Explanation/Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877

 

NEW QUESTION 57
You have an Apache Kafka cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?

A. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.B. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read from PubSub and write to GCS.C. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.D. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read from PubSub and write to GCS.

Answer: A

Explanation:
Explanation/Reference:

 

NEW QUESTION 58
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