GCP-DE - Newest Certification Data Engineer Test Questions

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability
Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows
Designing data processing systems	20%	- Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design - Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery
Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage

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Reliable Certification GCP-DE Test Questions Covers the Entire Syllabus of GCP-DE

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Google Data Engineer Sample Questions (Q22-Q27):

NEW QUESTION # 22
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
D. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

Answer: B

NEW QUESTION # 23
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: date#data_pointColumn data: device_id
B. Rowkey: device_idColumn data: date, data_point
C. Rowkey: dateColumn data: device_id, data_point
D. Rowkey: date#device_idColumn data: data_point
E. Rowkey: data_pointColumn data: device_id, date

Answer: E

NEW QUESTION # 24
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 using BigQuery's default encoding.
B. The CSV data has not gone through an ETL phase before loading into BigQuery.
C. The CSV data loaded in BigQuery is not flagged as CSV.
D. The CSV data has invalid rows that were skipped on import.

Answer: D

NEW QUESTION # 25
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Sessions
B. Windows
C. OutputCriteria
D. Triggers

Answer: D

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

NEW QUESTION # 26
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
D. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Answer: D

NEW QUESTION # 27
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