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To become a Google Professional-Data-Engineer, candidates need to pass the certification exam. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that assess a candidate's ability to design, build, and manage data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam can be taken online or in-person at a proctored testing center. Candidates have two hours to complete the exam, and they must score at least 70% to pass.

The Google Professional-Data-Engineer Exam is designed for data engineers, data analysts, and data scientists who work with data processing systems and data analytics solutions on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification demonstrates their ability to design, build, and manage scalable, reliable, and cost-effective data solutions on Google Cloud.

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The Google Professional-Data-Engineer Exam covers a wide range of topics, including data processing systems, data analysis, machine learning, and data security on Google Cloud Platform. Candidates are expected to have a thorough understanding of these topics and be able to apply them in real-world scenarios.

Google Certified Professional Data Engineer Exam Sample Questions (Q318-Q323):

NEW QUESTION # 318
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud.
You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Deploy a Cloud Dataproc cluster.
Use a standard persistent disk and 50% preemptible workers.
Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
B. Deploy a Cloud Dataproc cluster.
Use an SSD persistent disk and 50% preemptible workers.
Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
C. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances.
Install the Cloud Storage connector, and store the data in Cloud Storage.
Change references in scripts from hdfs:// to gs://
D. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.
Store data in HDFS.Change references in scripts from hdfs:// to gs://

Answer: A

Explanation:
Ask for cost effective so persistent disk are HDD which are cheaper in comparison to SSD.

NEW QUESTION # 319
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
B. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
C. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
D. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
E. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

Answer: C

NEW QUESTION # 320
Which Java SDK class can you use to run your Dataflow programs locally?

A. LocalRunner
B. LocalPipelineRunner
C. DirectPipelineRunner
D. MachineRunner

Answer: C

Explanation:
Explanation
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION # 321
You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM).
Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?

A. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS
B. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
C. Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage
D. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory

Answer: A

Explanation:
Its google recommended approach to use LocalDisk/HDFS to store Intermediate result and use Cloud Storage for initial and final results.

NEW QUESTION # 322
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

A. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
B. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
C. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
D. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.

Answer: A

NEW QUESTION # 323
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