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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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
Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines - Security and governance 1. Data encryption and compliance 2. IAM and access control
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
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

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Google Data Engineer Sample Questions (Q11-Q16):

NEW QUESTION # 11
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

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

Answer: B

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 # 12
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be multi-regional
B. In the event of an emergency, use the backup copy of the table.
C. Set the BigQuery dataset to be regiona
D. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
E. In the event of an emergency, use the backup copy of the table.
F. Set the BigQuery dataset to be regiona
G. In the event of an emergency, use a point-in-time snapshot to recover the data.
H. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
I. In the event of an emergency, use a point-in-time snapshot to recover the data.
J. Set the BigQuery dataset to be multi-regional

Answer: G

NEW QUESTION # 13
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
B. Change the zone of your Cloud Dataflow pipeline to run in us-central1
C. Use a larger instance type for your Cloud Dataflow workers
D. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
E. Increase the number of max workers
F. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
G. Create a temporary table in Cloud Spanner that will act as a buffer for new data

Answer: C,D

NEW QUESTION # 14
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Write an Apache Beam pipeline that creates a BigQuery table per data
B. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
C. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
D. Partition the tables by a column containing a TIMESTAMP or DATE Type.
E. Re-create the tables using DD
F. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

Answer: C

NEW QUESTION # 15
When a Cloud Bigtable node fails, is lost.

A. the time dimension
B. the last transaction
C. all data
D. no data

Answer: D

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 16
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