最新的 Google Cloud Certified GCP-DE 免費考試真題:

1. You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

A) An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
B) An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
C) An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
D) An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination

2. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
C) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
D) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

3. Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
B) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
C) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
D) Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
E) Introduce data compression for each file to increase the rate file of file transfer.
F) Assemble 1,000 files into a tape archive (TAR) fil

4. You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
B) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
C) Then run the Hadoop utility to copy them do HDF
D) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
E) Replicate external Hive tables to the native ones.
F) Mount the Hive tables locally.
G) Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
H) Mount the Hive tables locally.
I) Replicate external Hive tables to the native ones.
J) Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
K) Mount the Hive tables from HDFS.
L) Load the ORC files into BigQuer

5. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A) Include multiple time series values within the row key
B) Keep the row keep as an 8 bit integer
C) Keep your row key reasonably short
D) Keep your row key as long as the field permits

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