PASS GUARANTEED TRUSTABLE PROFESSIONAL-DATA-ENGINEER - GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM TEST DATES

Pass Guaranteed Trustable Professional-Data-Engineer - Google Certified Professional Data Engineer Exam Test Dates

Pass Guaranteed Trustable Professional-Data-Engineer - Google Certified Professional Data Engineer Exam Test Dates

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Google Professional-Data-Engineer Certification Exam is highly respected in the industry and is a valuable asset for professionals who want to advance their careers in big data. Holding this certification demonstrates that a candidate has the skills and knowledge needed to design and build data processing systems on the Google Cloud Platform. It is also a testament to a candidate's dedication to advancing their skills and staying up-to-date with the latest technologies in the field.

To become a Google Certified Professional Data Engineer, a candidate must pass the certification exam, which costs $200. Professional-Data-Engineer Exam is available in English, Japanese, and Spanish and can be taken online or at a testing center. Professional-Data-Engineer exam is valid for two years, after which a candidate must recertify to maintain their certification.

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Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

Google Certified Professional Data Engineer Exam Sample Questions (Q132-Q137):

NEW QUESTION # 132
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. Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery. dataViewer role on the dataset.
  • B. Create authorized datasets to publish shared data in the subscribing team's project.
  • C. Use Analytics Hub to facilitate data sharing.
  • D. Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

Answer: D

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 # 133
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

  • A. Retry the query with exponential backoff, up to a cap of 15 minutes.
  • B. Retry the query every second until it comes back online to minimize staleness of data.
  • C. Issue a command to restart the database servers.
  • D. Reduce the query frequency to once every hour until the database comes back online.

Answer: A

Explanation:
https://cloud.google.com/sql/docs/mysql/manage-connections#backoff


NEW QUESTION # 134
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?

  • A. Workers and parameter servers
  • B. Workers
  • C. Masters, workers, and parameter servers
  • D. Parameter servers

Answer: A

Explanation:
Explanation
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node.
You may set TrainingInput.workerCount to specify the number of workers to use.
You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.
You can specify the type of machine for the master node, but you can't specify more than one master node.
Reference: https://cloud.google.com/ml-engine/docs/training-overview#job_configuration_parameters


NEW QUESTION # 135
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat
a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

  • A. Denormalize the data as must as possible.
  • B. Preserve the structure of the data as much as possible.
  • C. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
  • D. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
  • E. Use BigQuery UPDATE to further reduce the size of the dataset.

Answer: A,D


NEW QUESTION # 136
You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

  • A. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
  • B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
  • C. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
  • D. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Answer: C


NEW QUESTION # 137
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