GCP-DE Flexible Testing Engine - Real GCP-DE Braindumps

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Topic 2: 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
Topic 3: 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 4: 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

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Google Data Engineer Sample Questions (Q57-Q62):

NEW QUESTION # 57
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. 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.
B. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C. Introduce data compression for each file to increase the rate file of file transfer.
D. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
F. Assemble 1,000 files into a tape archive (TAR) fil

Answer: B,D

NEW QUESTION # 58
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, you must configure access through your application.
B. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
C. You can configure access control only at the project level.
D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

Answer: B

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 59
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Remove the features that have null values for more than 50% of the training records.
B. Instead of feeding in each feature individually, average their values in batches of 3.
C. Combine highly co-dependent features into one representative feature.
D. Eliminate features that are highly correlated to the output labels.

Answer: C

NEW QUESTION # 60
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
B. Create a view on the table to present to the virtualization tool.
C. Export the data into a Google Sheet for virtualization.
D. Create an additional table with only the necessary columns.

Answer: B

NEW QUESTION # 61
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, but reduce your dataset twice.
C. Use Cloud Vision AutoML with the existing dataset.
D. Train your own image recognition model leveraging transfer learning techniques.

Answer: C

NEW QUESTION # 62
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