GCP-DE Exam Assessment - GCP-DE Cert

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows
Topic 2: 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. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs
Topic 3: 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
Topic 4: Designing data processing systems	24%	- Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Planning for data security and compliance 3. Selecting appropriate storage solutions - Designing data pipelines 1. Designing for data transformation and enrichment 2. Planning for data migration and integration 3. Defining architecture for batch and streaming processing
Topic 5: 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

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Google Data Engineer Sample Questions (Q59-Q64):

NEW QUESTION # 59
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A. Sink API
B. ParDo
C. Source API
D. Data extraction

Answer: B

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION # 60
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
C. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

Answer: B

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 61
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. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
C. 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.
D. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Answer: A

NEW QUESTION # 62
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Answer: C

NEW QUESTION # 63
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
B. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
C. Create an application that provides an AP
D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
E. Create an application that writes to a Cloud SQL database to store the dat
F. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Answer: C

NEW QUESTION # 64
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