無料Professional-Data-Engineer試験ブレーン問題集認定ガイド問題と解答 [Q103-Q127]

無料Professional-Data-Engineer試験ブレーン問題集認定ガイド問題と解答

Professional-Data-Engineer認定概要最新のProfessional-Data-EngineerPDF問題集

質問 # 103
Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

• A. Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.
• B. Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.
• C. Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.
• D. Use a Cloud SQL for PostgreSQL highly available instance first, and bagtable with US. Europe, and Asiareplication alter securing funding

正解：C

解説：
https://cloud.google.com/spanner/docs/instance-configurations#tradeoffs_regional_versus_multi- region_configurations

質問 # 104
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

• A. The CSV data has not gone through an ETL phase before loading into BigQuery.
• B. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
• C. The CSV data loaded in BigQuery is not flagged as CSV.
• D. The CSV data has invalid rows that were skipped on import.

正解：B

質問 # 105
Which action can a Cloud Dataproc Viewer perform?

• A. Submit a job.
• B. Delete a cluster.
• C. List the jobs.
• D. Create a cluster.

正解：C

解説：
A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
Reference:
https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_operations
_summary

質問 # 106
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. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
• C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
• D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API.
  Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

正解：D

質問 # 107
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?

• A. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
• B. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
• C. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
• D. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

正解：B

解説：
Explanation

質問 # 108
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

• A. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
• B. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
• C. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
• D. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.

正解：D

解説：
By creating an authorized view one assures that the data is current and avoids taking more storage space (and cost) in order to share a dataset. B and D are not cost optimal and C does not guarantee that the data is kept updated.

質問 # 109
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 Pig.
• B. Increase the size of the Hadoop cluster.
• C. Rewrite the job in Apache Spark.
• D. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

正解：A

質問 # 110
Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?

• A. Use Identity and Access Management conditions to ensure that only users and service accounts in project A can access resources in project.
• B. Configure VPC Service Controls in the organization with a perimeter around the VPC of project A.
• C. Add firewall rules in project A so only traffic from the VPC in project A is permitted.
• D. Configure VPC Service Controls in the organization with a perimeter around project A.

正解：A

解説：
Identity and Access Management (IAM) is the recommended way to control access to Pub/Sub resources, such as topics and subscriptions. IAM allows you to grant roles and permissions to users and service accounts at the project level or the individual resource level. You can also use IAM conditions to specify additional attributes for granting or denying access, such as time, date, or origin. By using IAM conditions, you can ensure that only the resources in project A can access the data in the project A topic, regardless of the network configuration or the VPC Service Controls. You can also prevent project B and any future project from accessing the data in the project A topic by not granting them any roles or permissions on the topic.
Option A is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around the VPC of project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option B is not a good solution, as firewall rules are used to control the ingress and egress traffic to and from the VPC network of a project. Firewall rules do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, adding firewall rules in project A to only permit traffic from the VPC in project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option C is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. References: Access control with IAM | Cloud Pub/Sub Documentation | Google Cloud, [Using IAM Conditions | Cloud IAM Documentation | Google Cloud], [VPC Service Controls overview | Google Cloud], [Using VPC Service Controls | Google Cloud], [Pub/Sub tier capabilities | Memorystore for Redis | Google Cloud].

質問 # 111
You work for a financial institution that lets customers register online. As new customers register, their user data is sent to Pub/Sub before being ingested into BigQuery. For security reasons, you decide to redact your customers' Government issued Identification Number while allowing customer service representatives to view the original values when necessary. What should you do?

• A. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic hash.
• B. Use BigQuery column-level security. Set the table permissions so that only members of the Customer Service user group can see the SSN column.
• C. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token.
• D. Use BigQuery's built-in AEAD encryption to encrypt the SSN column. Save the keys to a new table that is only viewable by permissioned users.

正解：C

質問 # 112
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

• A. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
• B. Use .fromQuery operation to read specific fields from the table.
• C. Specify the Tableobject in the code.
• D. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

正解：B

解説：
BigQueryIO.read.from() directly reads the whole table from BigQuery. This function exports the whole table to temporary files in Google Cloud Storage, where it will later be read from. This requires almost no computation, as it only performs an export job, and later Dataflow reads from GCS (not from BigQuery).
BigQueryIO.read.fromQuery() executes a query and then reads the results received after the query execution. Therefore, this function is more time-consuming, given that it requires that a query is first executed (which will incur in the corresponding economic and computational costs).

質問 # 113
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

• A. Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
• B. Check the dashboard application to see if it is not displaying correctly.
• C. Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.
• D. Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.

正解：A

解説：
Explanation:

質問 # 114
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data.
Which two actions should you take? (Choose two.)

• A. Configure your Cloud Dataflow pipeline to use local execution
• B. Increase the number of nodes in the Cloud Bigtable cluster
• C. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
• D. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
• E. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

正解：D、E

解説：
A - Local execution is useful for testing and debugging purposes, especially if your pipeline can use smaller in-memory datasets.
B- https://cloud.google.com/dataflow/docs/guides/specifying-exec-params C- increases both read and write performance D- Flatten merges multiple PCollection objects into a single logical PCollection.
E- Consider using CoGroupByKey if you have multiple data sets that provide information about related things .

質問 # 115
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

• A. Migrate the workload to Google Cloud Dataflow
• B. Use a higher-memory node so that the job runs faster
• C. Use SSDs on the worker nodes so that the job can run faster
• D. Use pre-emptible virtual machines (VMs) for the cluster

正解：A

質問 # 116
Which of the following are examples of hyperparameters? (Select 2 answers.)

• A. Number of nodes in each hidden layer
• B. Weights
• C. Biases
• D. Number of hidden layers

正解：A、D

解説：
Explanation
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview

質問 # 117
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. Convert your batch BQ queries into interactive BQ queries.
• 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. Create an additional project to overcome the 2K on-demand per-project quota.

正解：C

解説：
Explanation
Explanation/Reference:
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

質問 # 118
You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?

• A. Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.
• B. Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.
• C. Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.
• D. Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.

正解：D

解説：
A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired.
Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. References:
* Materialized views, ML models in data warehouse - Google Cloud
* Data Engineering with Google Cloud Platform - Packt Subscription

質問 # 119
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

• A. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
• B. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
• C. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
• D. Increase the cluster size with more non-preemptible workers.

正解：B

質問 # 120
You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

• A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day.
• B. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day.
• C. Store your data in BigQuery. Use the metric as a primary key.
• D. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key.

正解：A

解説：
To store your data in a way that meets both access patterns, you should:
A). Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day. This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery's features such as partitioning, clustering, and caching.
B). Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key. This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs. BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness.
Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.
C). Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day. This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.
D). Store your data in BigQuery. Use the metric as a primary key. This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.

質問 # 121
Which methods can be used to reduce the number of rows processed by BigQuery?

• A. Splitting tables into multiple tables; putting data in partitions
• B. Putting data in partitions; using the LIMIT clause
• C. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause
• D. Splitting tables into multiple tables; using the LIMIT clause

正解：A

解説：
Explanation
If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day.
If you use the LIMIT clause, BigQuery will still process the entire table.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

質問 # 122
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

• A. 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators
  2 Use a single shared DAG for all tables that need to go through the pipeline
  3 Schedule the DAG to run hourly
• B. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators
  2 Use a single shared DAG for all tables that need to go through the pipeline.
  3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
• C. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
  2 Create a separate DAG for each table that needs to go through the pipeline
  3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
• D. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators
  2 Create a separate DAG for each table that needs to go through the pipeline
  3 Schedule the DAGs to run hourly

正解：C

解説：
This option is the most efficient and maintainable workflow for your use case, as it allows you to process each table independently and trigger the DAGs only when new data arrives in the Cloud Storage bucket. By using the Dataproc and BigQuery operators, you can easily orchestrate the load and transformation jobs for each table, and leverage the scalability and performance of these services12. By creating a separate DAG for each table, you can customize the transformation logic and parameters for each table, and avoid the complexity and overhead of a single shared DAG3. By using a Cloud Storage object trigger, you can launch a Cloud Function that triggers the DAG for the corresponding table, ensuring that the data is processed as soon as possible and reducing the idle time and cost of running the DAGs on a fixed schedule4 .
Option A is not efficient, as it runs the DAG hourly regardless of the data arrival, and it uses a single shared DAG for all tables, which makes it harder to maintain and debug. Option C is also not efficient, as it runs the DAGs hourly and does not leverage the Cloud Storage object trigger. Option D is not maintainable, as it uses a single shared DAG for all tables, and it does not use the Cloud Storage operator, which can simplify the data ingestion from the bucket. References:
* 1: Dataproc Operator | Cloud Composer | Google Cloud
* 2: BigQuery Operator | Cloud Composer | Google Cloud
* 3: Choose Workflows or Cloud Composer for service orchestration | Workflows | Google Cloud
* 4: Cloud Storage Object Trigger | Cloud Functions Documentation | Google Cloud
* [5]: Triggering DAGs | Cloud Composer | Google Cloud
* [6]: Cloud Storage Operator | Cloud Composer | Google Cloud

質問 # 123
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

• A. BigQuery cannot be used as a sink
• B. Only batch
• C. Both batch and streaming
• D. Only streaming

正解：C

解説：
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts Reference: https://cloud.google.com/dataflow/model/bigquery-io

質問 # 124
Your neural network model is taking days to train. You want to increase the training speed. What can you do?

• A. Subsample your training dataset.
• B. Subsample your test dataset.
• C. Increase the number of layers in your neural network.
• D. Increase the number of input features to your model.

正解：C

質問 # 125
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

• A. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.
• B. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
• C. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
• D. Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.

正解：A

質問 # 126
Which of the following statements about Legacy SQL and Standard SQL is not true?

• A. You need to set a query language for each dataset and the default is Standard SQL.
• B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
• C. One difference between the two query languages is how you specify fully-qualified table names (i.e.
  table names that include their associated project name).
• D. Standard SQL is the preferred query language for BigQuery.

正解：A

解説：
Explanation
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released.
In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql

質問 # 127
......

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