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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage
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: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion - Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing

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Google Data Engineer Sample Questions (Q64-Q69):

NEW QUESTION # 64
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 count
C. Trigger based on element size in bytes
D. Trigger that is a combination of other triggers

Answer: C

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

Answer: A

NEW QUESTION # 66
Which of these sources can you not load data into BigQuery from?

A. Google Drive
B. Google Cloud Storage
C. Google Cloud SQL
D. File upload

Answer: C

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data

NEW QUESTION # 67
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. Preserve the structure of the data as much as possible.
B. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
C. Use BigQuery'ssupport for external data sources to query.
D. Use BigQuery UPDATE to further reduce the size of the dataset.
E. Denormalize the data as must as possible.
F. Develop a data pipeline where status updates are appended to BigQuery instead of updated.

Answer: B,F

NEW QUESTION # 68
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create a table on Cloud SQL, and insert and delete rows with the job information
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud Spanner, and insert and delete rows with the job information
D. Create an API using App Engine to receive and send messages to the applications

Answer: B

NEW QUESTION # 69
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