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To prepare for the Google Professional-Data-Engineer exam, candidates can take advantage of the many resources available online. Google offers a variety of training courses and study materials, including online courses, practice exams, and study guides. Additionally, there are many study groups and forums where candidates can connect with other professionals who are preparing for the exam. With the right preparation and dedication, professionals can achieve certification as a Google Certified Professional Data Engineer and demonstrate their expertise in Google Cloud Platform.
Google Professional-Data-Engineer Certification Exam is a highly prestigious certification program offered by Google for individuals who want to establish themselves as professional data engineers. Google Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design, build, operationalize, secure, and monitor data processing systems. It is designed for individuals who have experience working with data processing systems, data warehousing, and data analysis technologies.
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NEW QUESTION # 393
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?
Answer: C
Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source
/sink
Reference: https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners
/DirectPipelineRunner
NEW QUESTION # 394
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?
Answer: B
NEW QUESTION # 395
Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)
Answer: A,C,E
Explanation:
Explanation/Reference:
NEW QUESTION # 396
You work for a farming company. You have one BigQuery table named sensors, which is about 500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp. which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?
Answer: C
Explanation:
For a farming company with a sensor data table updated every 30 seconds, the goal is to minimize costs while facilitating weekly analytical queries. The best data model will effectively manage data storage, update frequency, and query performance.
* Partitioned Metrics Table:
* Creating a metrics table partitioned by timestamp optimizes query performance and storage costs.
* Partitioning by timestamp allows for efficient querying, especially for time-based analyses.
* Sensor ID Reference:
* Including a sensor_id column in the metrics table that points to the id column in the sensors table ensures data normalization.
* This structure avoids redundancy and maintains a clear relationship between sensors and their metrics.
* Using INSERT Statements:
* Using INSERT statements to append new metrics every 30 seconds is efficient and cost-effective.
* INSERT operations are more suitable than UPDATE operations for adding new data entries, especially at high frequencies.
* Joining Tables for Analysis:
* When running analytical queries, joining the partitioned metrics table with the sensors table as needed provides a comprehensive view of the data.
* This approach leverages BigQuery's powerful JOIN capabilities while keeping the data model normalized and efficient.
Google Data Engineer References:
* BigQuery Partitioned Tables
* BigQuery Best Practices
* Efficient Data Partitioning
* BigQuery Data Modeling
Using this data model, the farming company can manage its sensor data effectively, minimize costs, and perform weekly analytical queries with high efficiency.
NEW QUESTION # 397
You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily Your customers' information, such as their preferences, is hosted on a Cloud SQL for MySQL database Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers' information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time you want to keep the load on the Cloud SQL databases to a minimum. What should you do?
Answer: D
Explanation:
Datastream is a serverless Change Data Capture (CDC) and replication service that allows you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud Storage, Cloud SQL, and Pub/Sub. Datastream captures and delivers database changes in real-time, with minimal impact on the source database performance. Datastream also preserves the schema and data types of the source database, and automatically creates and updates the corresponding tables in BigQuery.
By using Datastream, you can replicate the required tables from both Cloud SQL databases to BigQuery, and keep them in sync with the source databases. This way, you can reduce the load on the Cloud SQL databases, as the marketing team can run their queries on the BigQuery tables instead of the Cloud SQL tables. You can also leverage the scalability and performance of BigQuery to query the customer behavioral data from Google Analytics and the customer information from the replicated tables. You can run the queries as frequently as needed, without worrying about the impact on the Cloud SQL databases.
Option A is not a good solution, as BigQuery federated queries allow you to query external data sources such as Cloud SQL databases, but they do not reduce the load on the source databases. In fact, federated queries may increase the load on the source databases, as they need to execute the query statements on the external data sources and return the results to BigQuery. Federated queries also have some limitations, such as data type mappings, quotas, and performance issues.
Option C is not a good solution, as creating a Dataproc cluster with Trino would require more resources and management overhead than using Datastream. Trino is a distributed SQL query engine that can connect to multiple data sources, such as Cloud SQL and BigQuery, and execute queries across them. However, Trino requires a Dataproc cluster to run, which means you need to provision, configure, and monitor the cluster nodes. You also need to install and configure the Trino connector for Cloud SQL and BigQuery, and write the queries in Trino SQL dialect. Moreover, Trino does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high.
Option D is not a good solution, as creating a job on Apache Spark with Dataproc Serverless would require more coding and processing power than using Datastream. Apache Spark is a distributed data processing framework that can read and write data from various sources, such as Cloud SQL and BigQuery, and perform complex transformations and analytics on them. Dataproc Serverless is a serverless Spark service that allows you to run Spark jobs without managing clusters. However, Spark requires you to write code in Python, Scala, Java, or R, and use the Spark connector for Cloud SQL and BigQuery to access the data sources. Spark also does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high. References: Datastream overview | Datastream | Google Cloud, Datastream concepts | Datastream | Google Cloud, Datastreamquickstart | Datastream | Google Cloud, Introduction to federated queries | BigQuery | Google Cloud, Trino overview | Dataproc Documentation | Google Cloud, Dataproc Serverless overview | Dataproc Documentation | Google Cloud, Apache Spark overview | Dataproc Documentation | Google Cloud.
NEW QUESTION # 398
......
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