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

（A）Cloud Bigtable

（B）Google Cloud Datastore

（C）Google Cloud Storage

（D）Google BigQuery

（A）Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

（B）Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

（C）Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

（D）Add a SideInput that returns a Boolean if the element is corrupt.

（A）The CSV data loaded in BigQuery is not using BigQuery's default encoding.

（B）The CSV data has not gone through an ETL phase before loading into BigQuery.

（C）The CSV data loaded in BigQuery is not flagged as CSV.

（D）The CSV data has invalid rows that were skipped on import.

（A）Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

（B）Use Analytics Hub to control data access, and provide third party companies with access to the dataset

（C）Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

（D）Create a 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.

（A）node

（B）type

（C）label

（D）zone

（A）The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.

（B）The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

（C）The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

（D）The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.

（A）Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

（B）Upgrade the external table to a BigLake table Enable metadata caching for the table.

（C）Change the storage class of the Hive partitioned data objects from Coldline to Standard.

（D）Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.

（A）Source API

（B）ParDo

（C）Sink API

（D）Data extraction

（A）Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

（B）Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

（C）Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

（D）Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

（A）Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sink

（B）Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasets

（C）Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery

（D）Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

（A）Rowkey: dateColumn data: device_id, data_point

（B）Rowkey: data_pointColumn data: device_id, date

（C）Rowkey: device_idColumn data: date, data_point

（D）Rowkey: date#data_pointColumn data: device_id

（E）Rowkey: date#device_idColumn data: data_point

（A）Enable the Object Versioning feature. Create a copy in a bucket in a different region.

（B）Set a retention policy. Set the default storage class to Archive for long-term digital preservation.

（C）Set a retention policy. Lock the retention policy.

（D）Enable the Object Versioning feature. Add a lifecycle rule.

（A）Spark

（B）Pig

（C）Hive

（D）YARN

（A）Cloud Storage

（B）Firebase Cloud Messaging

（C）Cloud Pub/Sub

（D）Apache Kafka

（A）Ask the source system to re-push the data to Pub/Sub, and subscribe to it.

（B）Set the topic retention policy to 30 days.

（C）Create a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.

（D）Set the subscriber retention policy to 30 days.

（A）1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

（B）1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table.

（C）1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts

（D）1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table.

（A）Cloud Datastore and Cloud Bigtable

（B）BigQuery and Cloud Bigtable

（C）BigQuery and Cloud Storage

（D）Cloud Bigtable and Cloud SQL

（A）Use BigQuery BI Engine with materialized views

（B）Use BigQuery Bl Engine with authorized views

（C）Use BigQuery BI Engine with streaming data.

（D）Use BigQuery Bl Engine with logical reviews