Google GCP-DE 問題集

質問 1：
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?
A. Cloud Bigtable
B. BigQuery
C. Cloud Datastore
D. Cloud SQL
正解：B

質問 2：
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
A. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
C. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
D. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
E. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
正解：A

質問 3：
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
A. Cloud Speech-to-Text API
B. Cloud Natural Language API
C. Dialogflow Enterprise Edition
D. Cloud AutoML Natural Language
正解：D

質問 4：
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
A. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
正解：D

質問 5：
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. Assemble 1,000 files into a tape archive (TAR) fil
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. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
正解：C,F

質問 6：
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
A. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
B. If that number falls below 4000, send an alert.
C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
D. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
E. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
F. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
G. Consume the stream of data in Cloud Dataflow using Kafka I
H. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
I. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
J. If that number falls below 4000, send an alert.
K. Consume the stream of data in Cloud Dataflow using Kafka I
L. Set a sliding time window of 1 hour every 5 minute
M. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
正解：M

質問 7：
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.
A. Denormalize the data as must as possible.
B. Use BigQuery'ssupport for external data sources to query.
C. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
E. Use BigQuery UPDATE to further reduce the size of the dataset.
F. Preserve the structure of the data as much as possible.
正解：C,D