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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. IAM and access control 2. Data encryption and compliance - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
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: 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

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Questions in desktop-based mock exams are identical to the real ones. Our practice exams give you options to change their durations and questions' numbers to polish your skills. You can easily assess your readiness with the assistance of results produced by the practice exam. This Data Engineer software records all your previous takes so you can identify your mistakes and overcome them before the final attempt. The Data Engineer (GCP-DE) desktop practice exam software works only on Windows operating system.

Google Data Engineer Sample Questions (Q74-Q79):

NEW QUESTION # 74
What are two methods that can be used to denormalize tables in BigQuery?

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

Answer: D

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 # 75
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. Assemble 1,000 files into a tape archive (TAR) fil
D. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E. 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.
F. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

Answer: B,F

NEW QUESTION # 76
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. Merge the transformed tables together with a SQL query
C. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
E. 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
F. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Answer: D

NEW QUESTION # 77
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
B. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
C. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
D. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Answer: C

NEW QUESTION # 78
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. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage

Answer: C

NEW QUESTION # 79
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