Google 공인 인증인 Google Certified Professional Data Engineer은 IT 업계에서 널리 인정받는 자격입니다. ExamPassdump의 Professional-Data-Engineer 연습문제 433문항으로 그 가치에 걸맞은 실력을 갖추시기 바랍니다.

Google Professional-Data-Engineer 시험 개요:

Google Professional-Data-Engineer 시험 요강 주제:

Google Professional-Data-Engineer 시험에 대한 질문과 답변 모음

최신 Google Cloud Certified Professional-Data-Engineer 무료샘플문제

문제 #1

Your financial services company has a critical daily reconciliation process that involves several distinct steps: fetching data from an external SFTP server, decrypting the files, loading them into Cloud Storage, and finally running a series of BigQuery SQL transformations. Each step has strict dependencies, and the entire process should notify you if not completed by 7:00 AM. Manual intervention for failures is costly and delays compliance reporting. You need a highly observable and robust solution that supports easy re-runs of individual steps if errors occur. What should you do?

A. Define a Cloud Composer DAG to orchestrate the SFTP fetch and decryption steps, and then use Cloud Scheduler to trigger a separate Dataflow job that handles the Cloud Storage load and BigQuery transformations and schedule to run daily.
B. Create a Cloud Composer DAG that includes a single BashOperator to execute a top-level shell script, which in turn calls individual scripts for each pipeline step. Upload the scripts to a Cloud Composer environment's DAGs folder, and configure it to run daily.
C. Implement a Cloud Composer DAG, with each step defined as a separate task using appropriate Airflow operators, and schedule the DAG for daily execution.
D. Develop a Cloud Composer DAG that includes a single PythonOperator to execute a Python script that runs each step sequentially, incorporating error handling and retries. Upload the scripts to a Cloud Composer environments DAGs folder, and configure it to run daily.

문제 #2

The Development and External teams have the project viewer Identity and Access Management (IAM) role in a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

A. Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project
B. Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level
C. Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level
D. Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

문제 #3

Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

A. dataflow.developer
B. dataflow.compute
C. dataflow.viewer
D. dataflow.worker

문제 #4

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

A. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.
B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataprep job.
C. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataproc job.
D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

문제 #5

You want to encrypt the customer data stored in BigQuery. You need to implement per-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

A. Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.
B. Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.
C. Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
D. Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.

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