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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%	- 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: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Topic 4: 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

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Google Data Engineer Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. BigQuery
B. Cloud Dataflow
C. Cloud Bigtable
D. Cloud Datastore

Answer: A

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

NEW QUESTION # 47
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Introduce data compression for each file to increase the rate file of file transfer.
B. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
C. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
D. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E. Assemble 1,000 files into a tape archive (TAR) fil
F. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

Answer: B,C

NEW QUESTION # 48
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. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
B. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
C. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
E. Merge the transformed tables together with a SQL query
F. 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

Answer: B

NEW QUESTION # 49
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 API by providing custom labels as recognition hints.
B. Use Cloud Vision AutoML with the existing dataset.
C. Use Cloud Vision AutoML, but reduce your dataset twice.
D. Train your own image recognition model leveraging transfer learning techniques.

Answer: B

NEW QUESTION # 50
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 slots/allocated_for_project
C. 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
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

Answer: A

NEW QUESTION # 51
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