Free PDF Google First-grade GCP-DE - Data Engineer Relevant Questions

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q19-Q24):

NEW QUESTION # 19
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. Use BigQuery UPDATE to further reduce the size of the dataset.
B. Denormalize the data as must as possible.
C. Preserve the structure of the data as much as possible.
D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
E. Use BigQuery'ssupport for external data sources to query.
F. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil

Answer: D,F

NEW QUESTION # 20
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Change "person" to "city.person".
B. Change "person" to "person.city".
C. Add ", UNNEST(person)" before the WHERE clause.
D. Add ", UNNEST(city)" before the WHERE clause.

Answer: C

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

NEW QUESTION # 21
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be regiona
B. In the event of an emergency, use the backup copy of the table.
C. In the event of an emergency, use a point-in-time snapshot to recover the data.
D. In the event of an emergency, use the backup copy of the table.
E. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
F. Set the BigQuery dataset to be multi-regional
G. Set the BigQuery dataset to be multi-regional
H. In the event of an emergency, use a point-in-time snapshot to recover the data.
I. Set the BigQuery dataset to be regiona
J. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu

Answer: H

NEW QUESTION # 22
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

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

Answer: A

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 # 23
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. Assemble 1,000 files into a tape archive (TAR) fil
B. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
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. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E. Introduce data compression for each file to increase the rate file of file transfer.
F. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Answer: B,F

NEW QUESTION # 24
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