Free Google Cloud Data Engineer Professional-Data-Engineer Exam Practice Test

You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

• Insert a Reshufle operation after each processing step, and monitor the execution details in the Dataflow console.
• Log debug information in each ParDo function, and analyze the logs at execution time.
• Insert output sinks after each key processing step, and observe the writing throughput of each block.
• Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks

An organization maintains a Google BigQuery dataset that contains tables with user-level dat

• They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?
• Create and share an authorized view that provides the aggregate results.
• Create and share a new dataset and view that provides the aggregate results.
• Create and share a new dataset and table that contains the aggregate results.
• Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost- effective solution. What should you do?

• Export the data from BigQuery into a new table that excludes the corrupted data.
• Migrate your data to multi-region BigQuery buckets.
• Access historical data by using time travel in BigQuery.
• Create a BigQuery table snapshot on a daily basis.

You need to deploy additional dependencies to all 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

You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write thedata to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

• Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.
• Adopt multi-regional Cloud Storage buckets in your architecture.
• Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.
• Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• Create a table in BigQuery, and append the new samples for CPU and memory to the table
• Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
• Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Your company currently runs a large on-premises cluster using Spark Hive and Hadoop Distributed File System (HDFS) in a colocation facility. The duster is designed to support peak usage on the system, however, many jobs are batch n nature, and usage of the cluster fluctuates quite dramatically.Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more servers offerings m order to take advantage of the cloud Because of the tuning of their contract renewal with the colocation facility they have only 2 months for their initial migration How should you recommend they approach thee upcoming migration strategy so they can maximize their cost savings in the cloud will still executing the migration in time?

• Migrate the workloads to Dataproc plus HOPS, modernize later
• Migrate the workloads to Dataproc plus Cloud Storage modernize later
• Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery
• Modernize the Spark workload for Dataflow and the Hive workload for BigQuery

You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

• Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
• Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
• Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
• Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.Which approach should you take?

• Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
• Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
• Use the NOW () function in BigQuery to record the event’s time.
• Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

• Load the data every 30 minutes into a new partitioned table in BigQuery.
• Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
• Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
• Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.