GCP-DE Test Braindumps, GCP-DE Free Test Questions

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| Section | Weight | Objectives |
|---|
| Topic 1: Ensuring solution quality | 20%-25% | - Data quality management
- 1. Maintain metadata and lineage
- 2. Implement data governance
- 3. Validate and clean data
- Security and compliance
- 1. Implement encryption and compliance controls
- 2. Protect sensitive data
- 3. Manage IAM and access control
|
| Topic 2: Designing data processing systems | 22%-27% | - Designing for data ingestion
- 1. Design scalable and reliable ingestion architecture
- 2. Design batch and streaming ingestion solutions
- 3. Select appropriate storage systems
- Designing data pipelines
- 1. Build ETL and ELT pipelines
- 2. Optimize pipeline performance and cost
- 3. Design orchestration workflows
|
| Topic 3: Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
- 1. Implement batch processing systems
- 2. Implement streaming data systems
- 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- Operationalizing systems
- 1. Implement fault tolerance and recovery
- 2. Manage logging and alerting
- 3. Monitor data pipelines and workloads
|
| Topic 4: Managing and optimizing solutions | 20%-25% | - Managing resources and costs
- 1. Optimize storage and compute usage
- 2. Tune query and pipeline performance
- 3. Control operational costs
- Reliability and scalability
- 1. Implement disaster recovery strategies
- 2. Scale data workloads efficiently
- 3. Design highly available systems
|
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Free PDF GCP-DE - Valid Data Engineer Test Braindumps
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Google Data Engineer Sample Questions (Q64-Q69):
NEW QUESTION # 64
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?
- A. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
- B. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
- C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
- D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
Answer: A
NEW QUESTION # 65
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)
- A. Configure your Cloud Dataflow pipeline to use local execution
- B. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
- C. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
- D. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
- E. Increase the number of nodes in the Cloud Bigtable cluster
Answer: B,D
NEW QUESTION # 66
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?
- A. Pipeline
- B. Transform
- C. Sink API
- D. PCollection
Answer: B
Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model
NEW QUESTION # 67
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- A. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- B. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
- C. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
- D. Consume the stream of data in Cloud Dataflow using Kafka I
- E. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- F. Set a sliding time window of 1 hour every 5 minute
- G. Consume the stream of data in Cloud Dataflow using Kafka I
- H. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
- I. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- J. If that number falls below 4000, send an alert.
- K. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
- L. If that number falls below 4000, send an alert.
- M. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
Answer: E
NEW QUESTION # 68
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?
- A. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
- B. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
- C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
- D. Use BigQuery to export the data for the table to a CSV fil
- E. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
- F. Add a new column called FullName to the Users tabl
Answer: A
NEW QUESTION # 69
......
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