Professional-Data-Engineer 無料問題集「Google Certified Professional Data Engineer」

（A）Create a Standard Tier Memorystore for Redis instance in a development environment. Initiate a manual failover by using the force-data-loss data protection mode.

（B）Create a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-data-loss data protection mode.

（C）Increase one replica to Redis instance in production environment. Initiate a manual failover by using the force-data-loss data protection mode.

（D）Initiate a manual tailover by using the limited-data-loss data protection mode to the Memorystore for Redis instance in the production environment.

（A）Increase the size of the dataset by collecting additional data.

（B）Match loan applicants with their social profiles to enable feature engineering.

（C）Train a linear regression to predict a credit default risk score.

（D）Remove the bias from the data and collect applications that have been declined loans.

（A）Google Drive

（B）Google Cloud Storage

（C）File upload

（D）Google Cloud SQL

（A）Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

（B）Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.

（C）Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

（D）Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.

（A）Apache Spark

（B）Apache Beam

（C）Apache Hadoop

（D）Apache Kafka

（A）The Cloud Bigtable cluster has too many nodes.

（B）The table's schema is not designed correctly.

（C）The workload isn't appropriate for Cloud Bigtable.

（D）There are issues with the network connection.

（A）Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

（B）Increase the number of max workers

（C）Change the zone of your Cloud Dataflow pipeline to run in us-central1

（D）Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

（E）Use a larger instance type for your Cloud Dataflow workers

（A）It's not possible to give access to only the first three columns of a table.

（B）Predefined role

（C）Primitive role

（D）Authorized view

（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. Reference: 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, reference 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. Reference: 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. Reference: 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.

（A）Read and write to Google Cloud Storage; write to Google Cloud Logging

（B）Execute to Google Cloud Storage; write to Google Cloud Logging

（C）Execute to Google Cloud Storage; execute to Google Cloud Logging

（D）Write to Google Cloud Storage; read to Google Cloud Logging

（A）Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.

（B）Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer..

（C）Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.

（D）Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

（A）Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

（B）Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

（C）Provide latitude and longtitude as input vectors to your neural net.

（D）Create a numeric column from a feature cross of latitude and longtitude.

（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 small set of generalized charts and tables bound to criteria filters that allow value selection.

（C）Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.

（D）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.

（A）Use a timestamp range filter in the query to fetch the customer's data for a specific range.

（B）Schedule a job daily to scan the data in the table and delete data older than 30 days.

（C）Set the expiring values of the column families to 29 days and keep the number of versions to 1.

（D）Set the expiring values of the column families to 30 days and set the number of versions to 2.

（A）Use Cloud Dataprep with recipes to detect errors and perform transformations.

（B）Use federated tables in BigQuery with queries to detect errors and perform transformations.

（C）Use Cloud Dataflow with Beam to detect errors and perform transformations.

（D）Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.