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You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

A.

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

B.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

C.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

D.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.

The data scientists have written the following code to read the data for a new key features in the logs.

BigQueryIO.Read

.named(“ReadLogData”)

.from(“clouddataflow-readonly:samples.log_data”)

You want to improve the performance of this data read. What should you do?

A.

Specify the TableReference object in the code.

B.

Use .fromQuery operation to read specific fields from the table.

C.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

D.

Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

A.

Add a node to the MySQL cluster and build an OLAP cube there.

B.

Use an ETL tool to load the data from MySQL into Google BigQuery.

C.

Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

D.

Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

A.

Create a Google Cloud Dataflow job to process the data.

B.

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

C.

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

D.

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

E.

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

A.

Send the data to Google Cloud Datastore and then export to BigQuery.

B.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

C.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

D.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

A.

Put the data into Google Cloud Storage.

B.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

C.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

D.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

A.

Redefine the schema by evenly distributing reads and writes across the row space of the table.

B.

The performance issue should be resolved over time as the site of the BigDate cluster is increased.

C.

Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

D.

Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.