Free PDF Google - GCP-DE - Trustable Valid Data Engineer Exam Labs

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows - Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing
Topic 2: Designing data processing systems	20%	- Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery - Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design
Topic 3: 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. BigQuery ML usage 2. Vertex AI integration for pipelines
Topic 4: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines

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Google Data Engineer Sample Questions (Q62-Q67):

NEW QUESTION # 62
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 1 minute
B. Windows at every 10 minutes
C. Windows at every 100 MB of data
D. Single, Global Window

Answer: D

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 63
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision AutoML, but reduce your dataset twice.
B. Use Cloud Vision AutoML with the existing dataset.
C. Train your own image recognition model leveraging transfer learning techniques.
D. Use Cloud Vision API by providing custom labels as recognition hints.

Answer: B

NEW QUESTION # 64
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
B. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
C. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
D. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Answer: A

NEW QUESTION # 65
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 has not gone through an ETL phase before loading into BigQuery.
B. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
C. The CSV data has invalid rows that were skipped on import.
D. The CSV data loaded in BigQuery is not flagged as CSV.

Answer: C

NEW QUESTION # 66
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataproc to run your transformation
B. Use Cloud Dataflow to run your transformation
C. Use Cloud Dataproc to run your transformation
D. Use the diagnose command to generate an operational output archiv
E. Monitor the job system lag with Stackdrive
F. Locate the bottleneck and adjust cluster resources.
G. Configure the job to use non-default Compute Engine machine types when needed.
H. Use Cloud Dataflow to run your transformation
I. Monitor CPU utilization for the cluste
J. Monitor the total execution time for a sampling of job
K. Use the default autoscaling setting for worker instances.
L. Resize the number of worker nodes in your cluster via the command line.

Answer: I

NEW QUESTION # 67
......

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