[2024] Pass Google Professional-Data-Engineer Exam in First Attempt Easily
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To prepare for the exam, candidates can take advantage of a range of resources, including Google Cloud’s official documentation, online courses, and practice exams. They can also join study groups and attend events and webinars to connect with other professionals and learn from their experiences.
Career Opportunities
The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.
NEW QUESTION # 21
Which Java SDK class can you use to run your Dataflow programs locally?
- A. MachineRunner
- B. DirectPipelineRunner
- C. LocalPipelineRunner
- D. LocalRunner
Answer: B
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/DirectPipelineRunner
NEW QUESTION # 22
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?
- A. WHERE
- B. BETWEEN
- C. LIMIT
- D. SELECT
Answer: D
Explanation:
Explanation
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference:
https://cloud.google.com/bigquery/launch-checklist#architecture_design_and_development_checklist
NEW QUESTION # 23
Which of these statements about BigQuery caching is true?
- A. Query results are cached even if you specify a destination table.
- B. There is no charge for a query that retrieves its results from cache.
- C. By default, a query's results are not cached.
- D. BigQuery caches query results for 48 hours.
Answer: B
Explanation:
Explanation
When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table.
Reference: https://cloud.google.com/bigquery/querying-data#query-caching
NEW QUESTION # 24
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 dat
a. 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 additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage
- C. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
- D. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
Answer: D
NEW QUESTION # 25
You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?
- A. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
- B. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
- C. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore
- D. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore
Answer: C
Explanation:
A hopping window is a type of sliding window that advances by a fixed period of time, producing overlapping windows. This is suitable for the scenario where the system needs to aggregate data for the last 30 seconds, every 2 seconds, and provide real-time updates. A Dataflow pipeline can implement the hopping window logic using Apache Beam, and process both streaming and batch data sources. Memorystore is a low-latency, in-memory data store that can serve the aggregated data to the visualization layer. BigQuery is not a good choice for this scenario, as it is not optimized for low-latency queries and frequent updates.
NEW QUESTION # 26
Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?
- A. Cloud Dataproc
- B. Cloud Dataprep
- C. Cloud Composer
- D. Cloud Dataflow
Answer: C
Explanation:
Cloud Composer uses airflow which is open source and can help to orchestrate jobs.
NEW QUESTION # 27
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.
What is the most likely cause of this problem?
- A. The CSV data loaded in BigQuery is not flagged as CSV.
- B. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
- C. The CSV data has not gone through an ETL phase before loading into BigQuery.
- D. The CSV data has invalid rows that were skipped on import.
Answer: D
NEW QUESTION # 28
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?
- A. Have the data acquisition devices publish data to Cloud Pub/Sub.
- B. Deploy small Kafka clusters in your data centers to buffer events.
- C. Establish a Cloud Interconnect between all remote data centers and Google.
- D. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
Answer: B
NEW QUESTION # 29
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?
- A. Create a Google Cloud Dataflow job to process the data.
- B. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
- C. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
- D. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
- E. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
Answer: A
NEW QUESTION # 30
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?
- A. Dataproc Worker
- B. Dataproc Viewer
- C. Dataproc Editor
- D. Dataproc Runner
Answer: A
Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference: https://cloud.google.com/dataproc/docs/concepts/service-
accounts#important_notes
NEW QUESTION # 31
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?
- A. Specify the TableReference object in the code.
- B. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
- C. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
- D. Use .fromQuery operation to read specific fields from the table.
Answer: C
NEW QUESTION # 32
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket
Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?
- A. Use a fixed-time window with a duration of 60 minutes.
- B. Use a sliding time window with a duration of 60 minutes.
- C. Use a global window with a time based trigger with a delay of 60 minutes.
- D. Use a session window with a gap time duration of 60 minutes.
Answer: C
NEW QUESTION # 33
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?
- A. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
- B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
- C. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
- D. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
Answer: D
NEW QUESTION # 34
You need to modernize your existing on-premises data strategy. Your organization currently uses.
* Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.
* Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.
You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?
- A. Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.
- B. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer..
- C. Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.
- D. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.
Answer: B
Explanation:
Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark workloads on Google Cloud. It is compatible with the open source ecosystem, so you can migrate your existing Hadoop clusters to Dataproc with minimal changes. Cloud Storage is a scalable, durable, and cost-effective object storage service that can replace HDFS for storing and accessing data. Cloud Storage offers interoperability with Hadoop through connectors, so you can use it as a data source or sink for your Dataproc jobs. Cloud Composer is a fully managed service that allows you to create, schedule, and monitor workflows using Apache Airflow. It is integrated with Google Cloud services, such as Dataproc, BigQuery, Dataflow, and Pub/Sub, so you can orchestrate your ETL pipelines across different platforms. Cloud Composer is compatible with your existing Airflow code, so you can migrate your existing orchestration processes to Cloud Composer with minimal changes.
The other options are not as suitable as Dataproc and Cloud Composer for this use case, because they either require more changes to your existing code, or do not meet your requirements. Dataflow is a fully managed service that allows you to create and run scalable data processing pipelines using Apache Beam. However, Dataflow is not compatible with your existing Hadoop code, so you would need to rewrite your ETL pipelines using Beam. Bigtable is a fully managed NoSQL database service that can handle large and complex data sets.
However, Bigtable is not compatible with your existing Hadoop code, so you would need to rewrite your queries and applications using Bigtable APIs. Cloud Data Fusion is a fully managed service that allows you to visually design and deploy data integration pipelines using a graphical interface. However, Cloud Data Fusion is not compatible with your existing Airflow code, so you would need to recreate your orchestration processes using Cloud Data Fusion UI. References:
* Dataproc overview
* Cloud Storage connector for Hadoop
* Cloud Composer overview
NEW QUESTION # 35
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)
- A. Change the zone of your Cloud Dataflow pipeline to run in us-central1
- B. Increase the number of max workers
- C. Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
- D. Use a larger instance type for your Cloud Dataflow workers
- E. Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
Answer: D,E
NEW QUESTION # 36
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRINGtype. 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. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on. - B. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP type. Reload the data.
- C. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause ensuring that the value of IS_NEWmust be true.
- D. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
- E. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now on.
Answer: C
NEW QUESTION # 37
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Create a table on Cloud Spanner, and insert and delete rows with the job information
- B. Create a table on Cloud SQL, and insert and delete rows with the job information
- C. Create an API using App Engine to receive and send messages to the applications
- D. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
Answer: C
Explanation:
Explanation/Reference: https://cloud.google.com/appengine/docs/standard/go/mail/sending-receiving-with-mail-api
NEW QUESTION # 38
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