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Professional-Data-Engineer Exam Score - Professional-Data-Engineer Exam Tutorials

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Google Professional-Data-Engineer exam is designed to test an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, developers, and other IT professionals who are responsible for designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer Exam covers a broad range of topics, including data processing, data warehousing, data analysis, and machine learning.

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Google Certified Professional Data Engineer Exam Sample Questions (Q297-Q302):

NEW QUESTION # 297
You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's dat a. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness What should you do?

A. Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.
B. Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery. dataViewer role on the dataset.
C. Use Analytics Hub to facilitate data sharing.
D. Create authorized datasets to publish shared data in the subscribing team's project.

Answer: A

Explanation:
To provide a cost-effective storage and processing solution that allows data scientists to explore data similarly to using the on-premises HDFS cluster with SQL on the Hive query engine, deploying a Dataproc cluster is the best choice. Here's why:
Compatibility with Hive:
Dataproc is a fully managed Apache Spark and Hadoop service that provides native support for Hive, making it easy for data scientists to run SQL queries on the data as they would in an on-premises Hadoop environment.
This ensures that the transition to Google Cloud is smooth, with minimal changes required in the workflow.
Cost-Effective Storage:
Storing the ORC files in Cloud Storage is cost-effective and scalable, providing a reliable and durable storage solution that integrates seamlessly with Dataproc.
Cloud Storage allows you to store large datasets at a lower cost compared to other storage options.
Hive Integration:
Dataproc supports running Hive directly, which is essential for data scientists familiar with SQL on the Hive query engine.
This setup enables the use of existing Hive queries and scripts without significant modifications.
Steps to Implement:
Copy ORC Files to Cloud Storage:
Transfer the ORC files from the on-premises HDFS cluster to Cloud Storage, ensuring they are organized in a similar directory structure.
Deploy Dataproc Cluster:
Set up a Dataproc cluster configured to run Hive. Ensure that the cluster has access to the ORC files stored in Cloud Storage.
Configure Hive:
Configure Hive on Dataproc to read from the ORC files in Cloud Storage. This can be done by setting up external tables in Hive that point to the Cloud Storage location.
Provide Access to Data Scientists:
Grant the data scientist team access to the Dataproc cluster and the necessary permissions to interact with the Hive tables.
Reference:
Dataproc Documentation
Hive on Dataproc
Google Cloud Storage Documentation

NEW QUESTION # 298
You are creating the CI'CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?

A. 1 Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing.
2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.
B. 1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.
2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.
C. 1. Use Cloud Build to build a container and the Kubemetes Pod Operator to deploy the code of the DAG to the Google Kubernetes Engine (GKE) cluster of the development instance for testing.
2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.
D. 1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.
2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

Answer: A

NEW QUESTION # 299
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Create separate views to cover each month, and query from these views
B. Convert the sharded tables into a single partitioned table
C. Convert all daily log tables into date-partitioned tables
D. Enable query caching so you can cache data from previous months

Answer: B

Explanation:
https://cloud.google.com/bigquery/docs/creating-partitioned-tables#converting_date- sharded_tables_into_ingestion-time_partitioned_tables

NEW QUESTION # 300
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and- shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery.
You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

A. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
B. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
C. Use Cloud ML Engine for training existing Spark ML models
D. Rewrite your models on TensorFlow, and start using Cloud ML Engine

Answer: B

NEW QUESTION # 301
You are working on a sensitive project involving private user data. You have set up a project on Google
Cloud Platform to house your work internally. An external consultant is going to assist with coding a
complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain
users' privacy?

A. Create a service account and allow the consultant to log on with it.
B. Create an anonymized sample of the data for the consultant to work with in a different project.
C. Grant the consultant the Viewer role on the project.
D. Grant the consultant the Cloud Dataflow Developer role on the project.

Answer: A

NEW QUESTION # 302
......

Because the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) test has a restricted time constraint, time management must be exercised to get success. Only with enough practice one can answer real Google Professional-Data-Engineer Exam Questions in a given amount of time. It has created three formats to aid Google Professional-Data-Engineer applicants in practicing and organizing their time for this aim.

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