最新のGCP-DE問題集で試験を準備する

Google GCP-DE 試験概要:

Google GCP-DE 試験シラバストピック:

Google Data Engineer 認定 GCP-DE 試験問題：

1. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A) Cloud Pub/Sub, Cloud Dataflow, and Local SSD
B) Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
C) Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
D) Cloud Pub/Sub, Cloud SQL, and Cloud Storage

2. Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A) Use feature engineering to add features for eyes, noses, and mouths to the input data.
B) Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C) Use K-means Clustering to detect faces in the pixels.
D) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

3. If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A) Do not use a production instance.
B) Before you test, run a heavy pre-test for several minutes.
C) Use at least 300 GB of data.
D) Run your test for at least 10 minutes.

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

5. 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 instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
B) 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
C) 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
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

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