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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Related Certifications: | Google Cloud Professional Cloud Architect Google Cloud Associate Cloud Engineer Google Cloud Professional Data Analyst |
| Exam Format: | Multiple select, Multiple choice |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 200 (plus tax where applicable) |
| Real Exam Qty: | 40 - 50 |
| Available Languages: | Japanese, English |
| Recommended Training: | Official Exam Guide Google Cloud Professional Data Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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Google Professional-Data-Engineer Certification Exam is a tough test that measures the competency of candidates in a variety of areas related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that cover topics such as data processing, data storage, data analysis, data visualization, and machine learning. Professional-Data-Engineer exam also includes hands-on tasks that require candidates to demonstrate their ability to use Google Cloud Platform tools and services to solve real-world data engineering problems.
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.
NEW QUESTION # 298
You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes.
You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?
Answer: D
NEW QUESTION # 299
You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?
Answer: D
Explanation:
To securely connect multiple applications with dynamic public IP addresses to a Cloud SQL instance using public IP, the Cloud SQL Auth proxy is the best solution. This proxy provides secure, authorized connections to Cloud SQL instances without the need to configure authorized networks or deal with IP whitelisting complexities.
Cloud SQL Auth Proxy:
The Cloud SQL Auth proxy provides secure, encrypted connections to Cloud SQL.
It uses IAM permissions and SSL to authenticate and encrypt the connection, ensuring data security in transit.
By using the proxy, you avoid the need to constantly update authorized networks as the proxy handles dynamic IP addresses seamlessly.
Authorized Network Configuration:
Leaving the authorized network empty means no IP addresses are explicitly whitelisted, relying solely on the Auth proxy for secure connections.
This approach simplifies network management and enhances security by not exposing the Cloud SQL instance to public IP ranges.
Dynamic IP Handling:
Applications with dynamic IP addresses can securely connect through the proxy without the need to modify authorized networks.
The proxy authenticates connections using IAM, making it ideal for environments where application IPs change frequently.
Google Data Engineer Reference:
Using Cloud SQL Auth Proxy
Cloud SQL Security Overview
Setting up the Cloud SQL Auth Proxy
By using the Cloud SQL Auth proxy, you ensure secure, authorized connections for applications with dynamic public IPs without the need for complex network configurations.
NEW QUESTION # 300
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison. What should you do?
Answer: D
NEW QUESTION # 301
You need to migrate 1 PB of data from an on-premises data center to Google Cloud. Data transfer time during the migration should take only a few hours. You want to follow Google- recommended practices to facilitate the large data transfer over a secure connection. What should you do?
Answer: A
Explanation:
https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets
NEW QUESTION # 302
You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?
Answer: D
Explanation:
To store your data in a way that meets both access patterns, you should:
* A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day. This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery's features such as partitioning, clustering, and caching.
* B. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key. This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs. BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness. Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.
* C. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day. This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.
* D. Store your data in BigQuery. Use the metric as a primary key. This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.
NEW QUESTION # 303
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