Google - Accurate GCP-DE Free Updates

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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
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
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
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

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Google Data Engineer Sample Questions (Q25-Q30):

NEW QUESTION # 25
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on time
B. Trigger based on element size in bytes
C. Trigger that is a combination of other triggers
D. Trigger based on element count

Answer: B

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 26
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A. Cloud Bigtable
B. Cloud Datastore
C. Cloud Spanner
D. Cloud SQL

Answer: D

NEW QUESTION # 27
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

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

Answer: C

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 # 28
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a SideInput that returns a Boolean if the element is corrupt.
B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Answer: D

NEW QUESTION # 29
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Merge the transformed tables together with a SQL query
B. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
C. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
E. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
F. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme

Answer: B

NEW QUESTION # 30
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