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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Applying data processing logic 3. Managing schema evolution - Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance
Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Designing data processing systems	24%	- Designing data pipelines 1. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment - Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Selecting appropriate storage solutions 3. Planning for data security and compliance
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Orchestrating and scheduling jobs 3. Ensuring reliability and recoverability

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Google Data Engineer Sample Questions (Q10-Q15):

NEW QUESTION # 10
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. Assemble 1,000 files into a tape archive (TAR) fil
C. 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.
D. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
F. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Answer: D,F

NEW QUESTION # 11
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B. Use the TABLE_DATE_RANGE function
C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D. Use the WHERE_PARTITIONTIME pseudo column

Answer: B

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap

NEW QUESTION # 12
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

A. BigQuery and Cloud Storage
B. Cloud Datastore and Cloud Bigtable
C. Cloud Bigtable and Cloud SQL
D. BigQuery and Cloud Bigtable

Answer: D

NEW QUESTION # 13
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Join tables into one table; 2) Use nested repeated fields
B. 1) Use a partitioned table; 2) Join tables into one table
C. 1) Split table into multiple tables; 2) Use a partitioned table
D. 1) Use nested repeated fields; 2) Use a partitioned table

Answer: A

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 14
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
C. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
D. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Answer: B

NEW QUESTION # 15
......

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