Google Professional-Data-Engineer 問題集

試験コード：Professional-Data-Engineer

試験名称：Google Certified Professional Data Engineer Exam

最近更新時間：2026-08-11

問題と解答：全433問

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質問 1：
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
A. Use K-means Clustering to detect faces in the pixels.
B. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 2：
You are monitoring your organization's data lake hosted on BigQuery. The ingestion pipelines read data from Pub/Sub and write the data into tables on BigQuery. After a new version of the ingestion pipelines is deployed, the daily stored data increased by 50%. The volumes of data in Pub/Sub remained the same and only some tables had their daily partition data size doubled. You need to investigate and fix the cause of the data increase. What should you do?
A. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Schedule daily SQL jobs to deduplicate the affected tables.
3. Share the deduplication script with the other operational teams to reuse if this occurs to other tables.
B. 1. Roll back the last deployment.
2. Restore the BigQuery tables to their content before the last release by using time travel.
3. Restart the Dataflow jobs and replay the messages by seeking the subscription to the timestamp of the release.
C. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Check the BigQuery Audit logs to find job IDs.
3. Use Cloud Monitoring to determine when the identified Dataflow jobs started and the pipeline code version.
4. When more than one pipeline ingests data into a table, stop all versions except the latest one.
D. 1. Check for code errors in the deployed pipelines.
2. Check for multiple writing to pipeline BigQuery sink.
3. Check for errors in Cloud Logging during the day of the release of the new pipelines.
4. If no errors, restore the BigQuery tables to their content before the last release by using time travel.
正解：C

質問 3：
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a hash bucket
B. Create a one-hot column
C. Create a multi-hot column
D. Create an embedding column
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage. You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:
- The old data can be deleted anytime.
- There is no predefined access pattern of the old data.
- The old data should be available instantly when accessed.
- There should not be any charges for data retrieval.
What should you do to optimize for cost?
A. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline, and 365 days to archive storage class. Delete old data as needed.
B. Create the bucket with the Autoclass storage class feature.
C. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 45 days to coldline, and 60 days to archive storage class. Delete old data as needed.
D. Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline, and 365 days to archive storage class. Delete old data as needed.
正解：B

質問 5：
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom, 555 X street
Tim, 553 Y street
Sam, 111 Z street
Which operation is best suited for the above data processing requirement?
A. Sink API
B. Data extraction
C. ParDo
D. Source API
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Your data science team needs to perform interactive SQL queries on large datasets stored in Apache Parquet format within a Cloud Storage bucket. The team is familiar with Apache Hive and wants to leverage existing HiveQL queries. You need to provide an environment for the team to run their interactive HiveQL queries directly against the data in Cloud Storage. You want to keep operational overhead to a minimum. What should you do?
A. Load the Parquet data into a BigQuery native table and use the BigQuery Connector for Hive to run the queries.
B. Configure BigQuery with an external table definition pointing to the Parquet files.
C. Deploy a Dataproc cluster with Hive services enabled.
D. Install and configure an Apache Hadoop and Hive cluster manually on a group of Compute Engine instances.
正解：C
解説: (Topexam メンバーにのみ表示されます)

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Google Professional-Data-Engineer 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
トピック 2	Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
トピック 3	Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
トピック 4	Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
トピック 5	Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.

参照：https://cloud.google.com/certification/data-engineer

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