[Oct 17, 2024] Professional-Data-Engineer Exam Brain Dumps - Study Notes and Theory
Pass Google Professional-Data-Engineer Test Practice Test Questions Exam Dumps
Google Professional-Data-Engineer certification is a valuable certification that can help professionals advance their careers in the field of data engineering. Google Certified Professional Data Engineer Exam certification demonstrates to employers that a candidate has the skills and knowledge needed to design and build data processing systems on Google Cloud Platform. It also shows that a candidate is committed to staying up-to-date with the latest technology trends and developments in the field of data engineering.
Google Certified Professional Data Engineer exam is a certification that validates the skills and knowledge of data engineers in designing and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is designed for individuals with experience in data processing, analysis, and transformation, who are seeking to demonstrate their proficiency in Google Cloud technologies and data engineering best practices.
To become a Google Certified Professional Data Engineer, candidates must have a strong foundation in data engineering concepts and technologies. They must also possess excellent problem-solving skills and have a deep understanding of data analysis and interpretation. Candidates can prepare for the exam by taking online courses, attending training programs, and practicing using real-world data scenarios.
NEW QUESTION # 17
Which of the following is NOT true about Dataflow pipelines?
- A. Dataflow pipelines can consume data from other Google Cloud services
- B. Dataflow pipelines can be programmed in Java
- C. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
- D. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
Answer: D
Explanation:
Explanation
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 18
You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?
- A. Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.
- B. Use Data Fusion to transform the data before loading it into BigQuery.
- C. Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.
- D. Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.
Answer: B
Explanation:
Data Fusion's advantages:
Visual interface: Offers a user-friendly interface for designing data pipelines without extensive coding, making it accessible to a wider range of users.
Built-in transformations: Includes a wide range of pre-built transformations to handle common data quality issues, such as:
Data type conversions
Data cleansing (e.g., removing invalid characters, correcting formatting) Data validation (e.g., checking for missing values, enforcing constraints) Data enrichment (e.g., adding derived fields, joining with other datasets) Custom transformations: Allows for custom transformations using SQL or Java code for more complex cleaning tasks.
Scalability: Can handle large datasets efficiently, making it suitable for processing CSV files with potential data quality issues.
Integration with BigQuery: Integrates seamlessly with BigQuery, allowing for direct loading of transformed data.
NEW QUESTION # 19
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
- A. Increase the size of the Google Persistent Disk on your server.
- B. Increase your network bandwidth from your datacenter to GCP.
- C. Increase the CPU size on your server.
- D. Increase your network bandwidth from Compute Engine to Cloud Storage.
Answer: B
NEW QUESTION # 20
Which Java SDK class can you use to run your Dataflow programs locally?
- A. LocalRunner
- B. LocalPipelineRunner
- C. DirectPipelineRunner
- D. MachineRunner
Answer: C
Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization. Useful for small local execution and tests
NEW QUESTION # 21
You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?
- A. Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks
- B. Log debug information in each ParDo function, and analyze the logs at execution time.
- C. Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.
- D. Insert output sinks after each key processing step, and observe the writing throughput of each block.
Answer: C
Explanation:
A Reshuffle operation is a way to force Dataflow to split the pipeline into multiple stages, which can help isolate the performance of each step and identify bottlenecks. By monitoring the execution details in the Dataflow console, you can see the time, CPU, memory, and disk usage of each stage, as well as the number of elements and bytes processed. This can help you diagnose where the pipeline is slowing down and optimize it accordingly. Reference:
1: Reshuffling your data
2: Monitoring pipeline performance using the Dataflow monitoring interface
3: Optimizing pipeline performance
NEW QUESTION # 22
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 Editor
- B. Dataproc Runner
- C. Dataproc Viewer
- D. Dataproc Worker
Answer: D
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 # 23
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. Cloud SQL
- B. BigQuery
- C. Cloud Datastore
- D. Cloud BigTable
Answer: D
Explanation:
Explanation/Reference: https://cloud.google.com/solutions/business-intelligence/
NEW QUESTION # 24
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
- A. A good use for the wide and deep model is a recommender system.
- B. The wide model is used for memorization, while the deep model is used for generalization.
- C. A good use for the wide and deep model is a small-scale linear regression problem.
- D. The wide model is used for generalization, while the deep model is used for memorization.
Answer: A,B
Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html
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 hopping 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 Memorystore
- C. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
- D. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore
Answer: B
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
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.
You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?
- A. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
- B. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- D. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
Answer: B
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 27
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?
- A. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
- B. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
- C. Increase the cluster size with more non-preemptible workers.
- D. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
Answer: D
NEW QUESTION # 28
You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?
- A. 1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Set a locked retention policy on the bucket.
3. Create a BigQuery external table on the exported files. - B. 1 Create a BigQuery table snapshot.
2 Restore the snapshot when you need to perform analytics. - C. 1 Create a BigQuery table clone.
2. Query the clone when you need to perform analytics. - D. 1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Enable versionmg on the bucket.
3. Create a BigQuery external table on the exported files.
Answer: A
Explanation:
This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. Reference:
Exporting table data | BigQuery | Google Cloud
Storage classes | Cloud Storage | Google Cloud
Retention policies and retention periods | Cloud Storage | Google Cloud Federated queries | BigQuery | Google Cloud
NEW QUESTION # 29
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?
- A. Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.
- B. Check the dashboard application to see if it is not displaying correctly.
- C. Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
- D. Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
Answer: D
NEW QUESTION # 30
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
- A. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
- B. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
- C. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
- D. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
Answer: C
Explanation:
A and B are wrong since don't notify anything to the monitoring tool.
C has no filter on what will be notified. We want only some tables.
NEW QUESTION # 31
The Dataflow SDKs have been recently transitioned into which Apache service?
- A. Apache Hadoop
- B. Apache Kafka
- C. Apache Spark
- D. Apache Beam
Answer: D
Explanation:
Explanation
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive Reference: https://cloud.google.com/dataflow/docs/
NEW QUESTION # 32
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)
- A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- B. Use Cloud Deployment Manager to automate access provision.
- C. Introduce resource hierarchy to leverage access control policy inheritance.
- D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
Answer: B,E
NEW QUESTION # 33
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