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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is an essential certification exam for professionals looking to advance their careers in the field of data engineering. Passing Professional-Data-Engineer Exam validates a candidate's expertise in designing, building, and managing data processing systems. It also demonstrates their ability to analyze and interpret data, make informed business decisions, and leverage cloud-based data processing systems to achieve business objectives.

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The Google Professional-Data-Engineer Exam consists of multiple-choice questions and is divided into four sections. The first section covers designing data processing systems, including the ability to design and implement data storage systems, data processing workflows, and data pipelines. The second section covers designing machine learning models, including the ability to design and implement machine learning algorithms, and to use machine learning to solve business problems. The third section covers designing data analysis tools, including the ability to design and implement data visualization tools, and to use data analysis to solve business problems. The fourth and final section covers designing applications and services, including the ability to design and implement cloud-based applications and services that meet the needs of businesses.

Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

Providing solution quality
Building data processing systems
Operationalizing machine learning models
Designing data processing systems

Google Certified Professional Data Engineer Exam Sample Questions (Q285-Q290):

NEW QUESTION # 285
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

A. Create a Cloud Dataproc Workflow Template
B. Create an initialization action to execute the jobs
C. Create a Directed Acyclic Graph in Cloud Composer
D. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

Answer: A

Explanation:
Explanation/Reference: https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows

NEW QUESTION # 286
You want to encrypt the customer data stored in BigQuery. You need to implement for-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. Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
B. Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.
C. Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.
D. Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.

Answer: B

Explanation:
To implement for-user crypto-deletion and ensure that customer data stored in BigQuery is encrypted, using native Google Cloud features, the best approach is to use Customer-Managed Encryption Keys (CMEK) with Cloud Key Management Service (KMS). Here's why:
* Customer-Managed Encryption Keys (CMEK):
* CMEK allows you to manage your own encryption keys using Cloud KMS. These keys provide additional control over data access and encryption management.
* Associating a CMEK with a BigQuery table ensures that data is encrypted with a key you manage.
* For-User Crypto-Deletion:
* For-user crypto-deletion can be achieved by disabling or destroying the CMEK. Once the key is disabled or destroyed, the data encrypted with that key cannot be decrypted, effectively rendering it unreadable.
* Native Integration:
* Using CMEK with BigQuery is a native feature, avoiding the need for custom encryption solutions. This simplifies the management and implementation of encryption and decryption processes.
Steps to Implement:
* Create a CMEK in Cloud KMS:
* Set up a new customer-managed encryption key in Cloud KMS.
* Associate the CMEK with BigQuery Tables:
* When creating a new table in BigQuery, specify the CMEK to be used for encryption.
* This can be done through the BigQuery console, CLI, or API.
Reference Links:
* BigQuery and CMEK
* Cloud KMS Documentation
* Encrypting Data in BigQuery

NEW QUESTION # 287
When you design a Google Cloud Bigtable schema it is recommended that you _________.

A. Create schema designs that are based on a relational database design
B. Avoid schema designs that are based on NoSQL concepts
C. Avoid schema designs that require atomicity across rows
D. Create schema designs that require atomicity across rows

Answer: C

Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 288
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
B. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.
C. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the
default autoscaling setting for worker instances.
D. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Answer: A

NEW QUESTION # 289
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

A. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
B. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
C. Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.
D. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.

Answer: D

Explanation:
https://cloud.google.com/iam/docs/roles-audit-logging#scenario_external_auditors

NEW QUESTION # 290
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