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質問 # 77
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?
- A. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
- B. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
- C. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
- D. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. the column TS instead of the column DT from now on.
- E. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
正解：C
質問 # 78
You need to compose visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute) The report must not be more than 3 hours delayed from live data.
The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
- A. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
- B. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
- C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
- D. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
正解：D
質問 # 79
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?
- A. Create an initialization action to execute the jobs
- B. Create a Directed Acyclic Graph in Cloud Composer
- C. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster
- D. Create a Cloud Dataproc Workflow Template
正解：D
解説：
Explanation/Reference: https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows
質問 # 80
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?
- A. create a third instance and sync the data from the two storage types via batch jobs
- B. export the data from the existing instance and import the data into a new instance
- C. the selection is final and you must resume using the same storage type
- D. run parallel instances where one is HDD and the other is SDD
正解：B
解説：
Explanation
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd-
質問 # 81
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 three.)
- A. Cassandra
- B. HBase
- C. MySQL
- D. Redis
- E. HDFS with Hive
- F. MongoDB
正解：B、E、F
質問 # 82
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 Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.
- B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.
- C. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- D. Consume the stream of data in Cloud Dataflow using Kafka IO. 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.
正解：B
質問 # 83
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?
- A. Retry the query every second until it comes back online to minimize staleness of data.
- B. Retry the query with exponential backoff, up to a cap of 15 minutes.
- C. Reduce the query frequency to once every hour until the database comes back online.
- D. Issue a command to restart the database servers.
正解：B
解説：
https://cloud.google.com/sql/docs/mysql/manage-connections#backoff
質問 # 84
You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes.
You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?
- A. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and profile for the analytics workload.
- B. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.
- C. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
- D. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
正解：C
質問 # 85
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on
Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
- A. Create a dedicated service account, and use encryption at rest to reference your data stored in your
Compute Engine cluster instances as part of your API service calls. - B. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- C. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
- D. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
正解：B
質問 # 86
You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO.
You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects.
You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?
- A. Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.
- B. Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.
- C. Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.
- D. Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.
正解：D
解説：
* Option A is incorrect because VPC Network Peering alone does not enable connectivity to Cloud SQL instances with private IP addresses. You also need to configure private services access and allocate an IP address range for the service producer network1.
* Option B is incorrect because Cloud NAT does not support Cloud SQL instances with private IP addresses. Cloud NAT only provides outbound connectivity for resources that do not have public IP addresses, such as VMs, GKE clusters, and serverless instances2.
* Option C is correct because it allows you to use a Compute Engine instance as a proxy server to connect to the Cloud SQL database over the peered network. The proxy server does not need an external IP address because it can communicate with the Dataflow workers and the Cloud SQL instance using internal IP addresses. You need to install the Cloud SQL Auth proxy on the proxy server and configure it to use a service account that has the Cloud SQL Client role.
* Option D is incorrect because it requires you to assign public IP addresses to the Dataflow workers, which exposes the data to the public internet. This violates the requirement of ensuring that the data does not go through the public internet. Moreover, adding authorized networks does not work for Cloud SQL instances with private IP addresses.
質問 # 87
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?
- A. Keep your row key reasonably short
- B. Keep your row key as long as the field permits
- C. Keep the row keep as an 8 bit integer
- D. Include multiple time series values within the row key
正解：A
解説：
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server. Reference: https:// cloud.google.com/bigtable/docs/schema-design#row-keys
質問 # 88
Which of these is not a supported method of putting data into a partitioned table?
- A. Create a partitioned table and stream new records to it every day.
- B. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
- C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
正解：B
解説：
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
質問 # 89
You work for a large ecommerce company. You store your customers order data in Bigtable. You have a garbage collection policy set to delete the data after 30 days and the number of versions is set to 1. When the data analysts run a query to report total customer spending, the analysts sometimes see customer data that is older than 30 days. You need to ensure that the analysts do not see customer data older than 30 days while minimizing cost and overhead. What should you do?
- A. Set the expiring values of the column families to 29 days and keep the number of versions to 1.
- B. Set the expiring values of the column families to 30 days and set the number of versions to 2.
- C. Schedule a job daily to scan the data in the table and delete data older than 30 days.
- D. Use a timestamp range filter in the query to fetch the customer's data for a specific range.
正解：D
解説：
By using a timestamp range filter in the query, you can ensure that the analysts only see the customer data that is within the desired time range, regardless of the garbage collection policy1. This option is the most cost-effective and simple way to avoid fetching data that is marked for deletion by garbage collection, as it does not require changing the existing policy or creating additional jobs. You can use the Bigtable client libraries or the cbt CLI to apply a timestamp range filter to your read requests2.
Option A is not effective, as it increases the number of versions to 2, which may cause more data to be retained and increase the storage costs. Option C is not reliable, as it reduces the expiring values to 29 days, which may not match the actual data arrival and usage patterns. Option D is not efficient, as it requires scheduling a job daily to scan and delete the data, which may incur additional overhead and complexity. Moreover, none of these options guarantee that the data older than 30 days will be immediately deleted, as garbage collection is an asynchronous process that can take up to a week to remove the data3. Reference:
1: Filters | Cloud Bigtable Documentation | Google Cloud
2: Read data | Cloud Bigtable Documentation | Google Cloud
3: Garbage collection overview | Cloud Bigtable Documentation | Google Cloud
質問 # 90
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?
- A. Deploy the model and measure the real-world AUC; it's always higher because of generalization
- B. Train a classifier with deep neural networks, because neural networks would always beat SVMs
- C. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
- D. Perform hyperparameter tuning
正解：D
質問 # 91
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?
- A. Create an additional project to overcome the 2K on-demand per-project quota.
- B. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
- C. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
- D. Convert your batch BQ queries into interactive BQ queries.
正解：C
質問 # 92
Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?
- A. Rewrite the job in Apache Spark.
- B. Increase the size of the Hadoop cluster.
- C. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.
- D. Rewrite the job in Pig.
正解：D
質問 # 93
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?
- A. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
- B. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
- C. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
- D. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
正解：D
質問 # 94
You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?
- A. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery
- B. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
- C. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
- D. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
正解：A
質問 # 95
You work for an airline and you need to store weather data in a BigQuery table Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather data. You want to avoid storing unnecessary data and minimize costs. What should you do?
- A. Create a BigQuery table partitioned by datetime value of the weather date Set up partition expiration to
30 days. - B. Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.
- C. Create a BigQuery table where each record has an ingestion timestamp Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.
- D. Create a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.
正解：B
解説：
Partitioning a table by ingestion time means that the data is divided into partitions based on the time when the data was loaded into the table. This allows you to delete or archive old data by setting a partition expiration policy. You can specify the number of days to keep the data in each partition, and BigQuery automatically deletes the data when it expires. This way, you can avoid storing unnecessary data and minimize costs.
質問 # 96
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