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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and automating data workloads	18%	- Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems
Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost - Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation
Ensuring solution quality and reliability	17%	- Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues - Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing
Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Ingesting data from various sources 2. Orchestrating data workflows 3. Transforming and cleaning data
Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection - Designing for business requirements 1. Designing for scalability and elasticity 2. Designing for reliability and fault tolerance 3. Selecting appropriate storage solutions

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Google Certified Professional Data Engineer Exam Sample Questions (Q361-Q366):

NEW QUESTION # 361
An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

A. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format
B. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format
C. Write a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source
D. Use a standard Dataflow pipeline to store the raw data in BigQuery and then transform the format later when the data is used.

Answer: A

NEW QUESTION # 362
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?

A. Google Cloud Datastore
B. Cloud Bigtable
C. Google BigQuery
D. Google Cloud Storage

Answer: B

Explanation:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using- gcp

NEW QUESTION # 363
Cloud Bigtable is a recommended option for storing very large amounts of
____________________________?

A. single-keyed data with very high latency
B. multi-keyed data with very high latency
C. single-keyed data with very low latency
D. multi-keyed data with very low latency

Answer: C

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 364
You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards. For example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding. What should you do?

A. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery.
B. Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
C. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
D. Create a Spark job and submit it to Dataproc Serverless.

Answer: C

NEW QUESTION # 365
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

A. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
B. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
C. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage.
Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
D. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

Answer: D

NEW QUESTION # 366
......

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