Google Cloud Certified Professional-Data-Engineer

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Professional-Data-Engineer exam certification is one of the most important certification recently. When qualified by the Professional-Data-Engineer certification, you will get a good job easily with high salary. Besides, the career opportunities will be open for a certified person. Now, you can get the valid and best useful Professional-Data-Engineer exam training material. Professional-Data-Engineer will help you to strengthen your technical knowledge and allow you pass at your first try.

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Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

• Use of edge compute
• Hardware accelerators (e.g., GPU, TPU)
• Impact of dependencies of machine learning models
• Continuous evaluation
• Conversational experiences (e.g., Dialogflow)
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Deploying an ML pipeline
• Common sources of error (e.g., assumptions about data)
• ML APIs (e.g., Vision API, Speech API)
• Operationalizing machine learning models
• Distributed vs. single machine
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• Leveraging pre-built ML models as a service
• Ingesting appropriate data
• Choosing the appropriate training and serving infrastructure
• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
• Measuring, monitoring, and troubleshooting machine learning models

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

Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

Google Professional-Data-Engineer Exam Syllabus Topics:

Google Certified Professional Data Engineer Sample Questions:

Question #1
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
B. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
C. Convert the streaming insert code to batch load for individual messages.
D. Re-write the application to load accumulated data every 2 minutes.

Question #2
You need to create a SQL pipeline. The pipeline runs an aggregate SQL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?

A. Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable email notifications.
B. Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three failed executions.
C. Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.
D. Use the BigQueryInsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

Question #3
dataset.inventory_vm sample records:

You have an inventory of VM data stored in the BigQuery table. You want to prepare the data for regular reporting in the most cost-effective way. You need to exclude VM rows with fewer than 8 vCPU in your report. What should you do?

A. Create a materialized view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.
B. Create a view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.
C. Use Dataflow to batch process and write the result to another BigQuery table.
D. Create a view with a filter to drop rows with fewer than 8 vCPU, and use the UNNEST operator.

Question #4
You want to schedule a number of sequential load and transformation jobs. Data files will be added to a Cloud Storage bucket by an upstream process. There is no fixed schedule for when the new data arrives. Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery. The transformation jobs are different for every table. These jobs might take hours to complete. You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.
B. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Schedule the DAGs to run hourly.
C. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Schedule the DAG to run hourly.
D. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.

Question #5
You are on the data governance team and are implementing security requirements. You need to encrypt all your data in BigQuery by using an encryption key managed by your team. You must implement a mechanism to generate and store encryption material only on your on-premises hardware security module (HSM). You want to rely on Google managed solutions. What should you do?

A. Create the encryption key in the on-premises HSM. Create BigQuery resources and encrypt data while ingesting them into BigQuery.
B. Create the encryption key in the on-premises HSM, and import it into Cloud Key Management Service (Cloud HSM) key. Associate the created Cloud HSM key while creating the BigQuery resources.
C. Create the encryption key in the on-premises HSM and link it to a Cloud External Key Manager (Cloud EKM) key. Associate the created Cloud KMS key while creating the BigQuery resources.
D. Create the encryption key in the on-premises HSM, and import it into a Cloud Key Management Service (Cloud KMS) key. Associate the created Cloud KMS key while creating the BigQuery resources.

Solutions: