GCP - DE 2

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

• Eliminate features that are highly correlated to the output labels.

• Combine highly co-dependent features into one representative feature.

• Instead of feeding in each feature individually, average their values in batches of 3.

• Remove the features that have null values for more than 50% of the training records.

You are deploying a MySQL database workload onto Cloud SQL. The database must be able to scale up to support several readers from various geographic regions. The database must be highly available and meet low RTO and RPO requirements, even in the event of a regional outage. You need to ensure that interruptions to the readers are minimal during a database failover. What should you do?

• Create a highly available Cloud SQL instance in region Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions. Backup the Cloud SQL instances to a multi-regional Cloud Storage bucket. Restore the Cloud SQL backup to a new instance in another region when Region A is down

• Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in multiple regions. Promote one of the read replicas when region A is down.

• Create a highly available Cloud SQL instance in region A. Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions. Promote the read replica in region B when region A is down.

• a)       Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in the same region. Failover to the standby Cloud SQL instance when the primary instance fais.

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users privacy?

• Grant the consultant the Viewer role on the project.

• Grant the consultant the Cloud Dataflow Developer role on the project.

• Create a service account and allow the consultant to log on with it.

• Create an anonymized sample of the data for the consultant to work with in a different project.

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

• Assign global unique identifiers (GUID) to each data entry.

• Compute the hash value of each data entry, and compare it with all historical data.

• Store each data entry as the primary key in a separate database and apply an index.

• a)       Maintain a database table to store the hash value and other metadata for each data entry.

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?

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

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

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

• 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 startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup to serve customers globally. Your current goal is to optimize for cost, and your post-funding goal is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

• Use Cloud Spanner to configure a single region instance initially, and then configure multi-region Cloud Spanner instances after securing funding.

• Use a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US, Europe, and Asia replication after securing funding.

• Use a Cloud SQL for PostgreSQL zonal instance first, and Bigtable with US, Europe, and Asia after securing funding.

• Use a Cloud SQL for PostgreSQL zonal instance first, and Cloud SQL for PostgreSQL with highly available configuration after securing funding.

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?

• Create a Cloud Dataproc Workflow Template

• Create an initialization action to execute the jobs

• Create a Directed Acyclic Graph in Cloud Composer

• Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster.

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:

SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country

You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

• Users are running too many concurrent queries in the system

• The [myproject:mydataset.mytable] table has too many partitions

• Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

• Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew.

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

• a)       Use bq load to load a batch of sensor data every 60 seconds

• Use a Cloud Dataflow pipeline to stream data into the BigQuery table.

• Use the INSERT statement to insert a batch of data every 60 seconds.

• Use the MERGE statement to apply updates in batch every 60 seconds.

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose two.)

• Redis

• HBase

• HDFS with Hive.

• MongoDB