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Google Professional-Data-Engineer certification is a highly recognized credential in the field of data engineering. It is designed to demonstrate a candidate’s expertise in designing, building, and maintaining data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for experienced data professionals who are seeking to enhance their skills and knowledge in the areas of data engineering, cloud computing, and data analytics.

Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

Google Professional-Data-Engineer Exam is one of the most sought-after certifications in the world of tech. Professional-Data-Engineer exam is designed for data engineers who work with Google Cloud technologies and want to demonstrate their expertise in designing and building scalable data processing systems on the Google Cloud platform. Successful completion of the exam results in a Google Certified Professional Data Engineer certification, which is highly valued in the industry.

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Google Certified Professional Data Engineer Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network.
You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible.
What should you do?

• A. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.
• B. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.
• C. Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.
• D. Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.

Answer: B

Explanation:
Here's a detailed breakdown of why this solution is optimal and why others fall short:
Why Option C is the Best Solution:
* Kafka Connect Bridge:This bridge acts as a reliable and scalable conduit between your on-premises Kafka cluster and Google Cloud's Pub/Sub messaging service. It handles the complexities of securely transferring data over the interconnect link.
* Pub/Sub as a Buffer:Pub/Sub serves as a highly scalable buffer, decoupling the Kafka producer from the Dataflow consumer. This is crucial for handling fluctuations in message volume and ensuring smooth data flow even during spikes.
* Custom Dataflow Pipeline:Writing a custom Dataflow pipeline gives you the flexibility to implement any necessary transformations or enrichments to the data before it's written to BigQuery. This is often required in real-world streaming scenarios.
* Minimal Latency:By using Pub/Sub as a buffer and Dataflow for efficient processing, you minimize the latency between the data being produced in Kafka and being available for querying in BigQuery.
Why Other Options Are Not Ideal:
* Option A:Using a proxy host introduces an additional point of failure and can create a bottleneck, especially with high-throughput streaming.
* Option B:While Google-provided Dataflow templates can be helpful, they might lack the customization needed for specific transformations or handling complex data structures.
* Option D:Dataflow doesn't natively connect to on-premises Kafka clusters. Directly reading from Kafka would require complex networking configurations and could lead to performance issues.
Additional Considerations:
* Schema Management:Ensure that the schema of the data being produced in Kafka is compatible with the schema expected in BigQuery. Consider using tools like Schema Registry for schema evolution management.
* Monitoring:Set up robust monitoring and alerting to detect any issues in the pipeline, such as message backlogs or processing errors.
By following Option C, you leverage the strengths of Kafka Connect, Pub/Sub, and Dataflow to create a high- throughput, low-latency streaming pipeline that seamlessly integrates your on-premises Kafka data with BigQuery.

 

NEW QUESTION # 125
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage.
The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

• A. 1. Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage.2. Define a BigQuery external table for SQL processing.3. Use Dataproc Spark to process the Cloud Storage files.
• B. 1. Deploy a long-living Dataproc cluster with Apache Hive and Ranger enabled.2. Configure Ranger for column level security.3. Process with Dataproc Spark or Hive SQL.
• C. 1. Load the data to BigQuery tables.2. Create a taxonomy of policy tags in Data Catalog.3. Add policy tags to columns.4. Process with the Spark-BigQuery connector or BigQuery SQL.
• D. 1. Define a BigLake table.2. Create a taxonomy of policy tags in Data Catalog.3. Add policy tags to columns.4. Process with the Spark-BigQuery connector or BigQuery SQL.

Answer: D

Explanation:
The key requirements are:
Data on Cloud Storage (migrated from HDFS).
Processing with Spark and SQL.
Column-level security.
Cost-effective and scalable for a data mesh.
Let's analyze the options:
Option A (Load to BigQuery tables, policy tags, Spark-BQ connector/BQ SQL):
Pros: BigQuery native tables offer excellent performance. Policy tags provide robust column-level security managed centrally in Data Catalog. The Spark-BigQuery connector allows Spark to read from/write to BigQuery. BigQuery SQL is powerful. Scales well.
Cons: "Loading" the data into BigQuery means moving it from Cloud Storage into BigQuery's managed storage. This incurs storage costs in BigQuery and an ETL step. While effective, it might not be the most
"cost-effective" if the goal is to query data in place on Cloud Storage, especially for very large datasets.
Option B (Long-living Dataproc, Hive, Ranger):
Pros: Provides a Hadoop-like environment with Spark, Hive, and Ranger for column-level security.
Cons: "Long-living Dataproc cluster" is generally not the most cost-effective, as you pay for the cluster even when idle. Managing Hive and Ranger adds operational overhead. While scalable, it requires more infrastructure management than serverless options.
Option C (IAM at file level, BQ external table, Dataproc Spark):
Pros: Using Cloud Storage is cost-effective for storage. BigQuery external tables allow SQL access.
Cons: IAM at the file level in Cloud Storage does not provide column-level security. This option fails to meet a critical requirement.
Option D (Define a BigLake table, policy tags, Spark-BQ connector/BQ SQL):
Pros:BigLake Tables: These tables allow you to query data in open formats (like Parquet, ORC) on Cloud Storage as if it were a native BigQuery table, but without ingesting the data into BigQuery's managed storage.
This is highly cost-effective for storage.
Column-Level Security with Policy Tags: BigLake tables integrate with Data Catalog policy tags to enforce fine-grained column-level security on the data residing in Cloud Storage. This is a centralized and robust security model.
Spark and SQL Access: Data scientists can use BigQuery SQL directly on BigLake tables. The Spark- BigQuery connector can also be used to access BigLake tables, enabling Spark processing.
Cost-Effective & Scalable Data Mesh: This approach leverages the cost-effectiveness of Cloud Storage, the serverless querying power and security features of BigQuery/Data Catalog, and provides a clear path to building a data mesh by allowing different domains to manage their data in Cloud Storage while exposing it securely through BigLake.
Cons: Performance for BigLake tables might be slightly different than BigQuery native storage for some workloads, but it's designed for high performance on open formats.
Why D is superior for this scenario:
BigLake tables (Option D) directly address the need to keep data in Cloud Storage (cost-effective for a data lake) while providing strong, centrally managed column-level security via policy tags and enabling both SQL (BigQuery) and Spark (via Spark-BigQuery connector) access. This is more aligned with modern data lakehouse and data mesh architectures than loading everything into native BigQuery storage (Option A) if the data is already in open formats on Cloud Storage, or managing a full Hadoop stack on Dataproc (Option B).
Reference:
Google Cloud Documentation: BigLake > Overview. "BigLake lets you unify your data warehouses and data lakes. BigLake tables provide fine-grained access control for tables based on data in Cloud Storage, while preserving access through other Google Cloud services like BigQuery, GoogleSQL, Spark, Trino, and TensorFlow." Google Cloud Documentation: BigLake > Introduction to BigLake tables. "BigLake tables bring BigQuery features to your data in Cloud Storage. You can query external data with fine-grained security (including row- level and column-level security) without needing to move or duplicate data." Google Cloud Documentation: Data Catalog > Overview of policy tags. "You can use policy tags to enforce column-level access control for BigQuery tables, including BigLake tables." Google Cloud Blog: "Announcing BigLake - Unifying data lakes and warehouses" (and similar articles) highlight how BigLake enables querying data in place on Cloud Storage with BigQuery's governance features.

 

NEW QUESTION # 126
How can you get a neural network to learn about relationships between categories in a categorical feature?

• A. Create a one-hot column
• B. Create an embedding column
• C. Create a hash bucket
• D. Create a multi-hot column

Answer: B

Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it.
But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html

 

NEW QUESTION # 127
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

• A. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
• B. Use the NOW () function in BigQuery to record the event's time.
• C. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
• D. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Answer: D

Explanation:
Explanation:

 

NEW QUESTION # 128
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?

• A. Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.
• B. Create BigQuery connections to both Cloud SQL databases Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.
• C. Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.
• D. Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.

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, Datastream quickstart | 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 # 129
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