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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	20%	- Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage - Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection
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
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
Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage - Security and governance 1. IAM and access control 2. Data encryption and compliance

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Google Data Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
B. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
C. Denormalize the data as must as possible.
D. Use BigQuery UPDATE to further reduce the size of the dataset.
E. Preserve the structure of the data as much as possible.
F. Use BigQuery'ssupport for external data sources to query.

Answer: A,B

NEW QUESTION # 67
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 Dataflow to run your transformation
B. Monitor CPU utilization for the cluste
C. Use Cloud Dataflow to run your transformation
D. Resize the number of worker nodes in your cluster via the command line.
E. Monitor the job system lag with Stackdrive
F. Use the default autoscaling setting for worker instances.
G. Use Cloud Dataproc to run your transformation
H. Use the diagnose command to generate an operational output archiv
I. Monitor the total execution time for a sampling of job
J. Use Cloud Dataproc to run your transformation
K. Configure the job to use non-default Compute Engine machine types when needed.
L. Locate the bottleneck and adjust cluster resources.

Answer: B

NEW QUESTION # 68
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

A. Cloud Bigtable and Cloud SQL
B. BigQuery and Cloud Storage
C. BigQuery and Cloud Bigtable
D. Cloud Datastore and Cloud Bigtable

Answer: C

NEW QUESTION # 69
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 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
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 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

Answer: A

NEW QUESTION # 70
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
B. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
C. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
E. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

Answer: D

NEW QUESTION # 71
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