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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Transforming and cleaning data
  • 3. Orchestrating data workflows
- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
Topic 2: Designing data processing systems20%- 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
Topic 3: Maintaining and automating data workloads18%- Resource optimization
  • 1. Cost management and resource allocation
  • 2. Choosing appropriate compute and storage options
- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
Topic 4: Operationalizing machine learning models20%- 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
Topic 5: Ensuring solution quality and reliability17%- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads
- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation

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Google Certified Professional Data Engineer Exam Sample Questions (Q284-Q289):

NEW QUESTION # 284
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet- of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required. You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

Answer: C,D,F

Explanation:
A). Redis is a key-value store (and in many cases used as in-memory and non persistent cache).
It is not designed for "100TB per year" of highly available storage.
B). HBase is similar to Google Bigtable, fits the requirements perfectly: highly available, scalable and with very low latency.
C). MySQL is a relational DB, designed precisely for ACID transactions and not for the stated requirements.
Also, growth may be an issue.
D). MongoDB is a document-db used for high volume data and maintains currently used data in RAM, so performance is usually really good. Should also fit the requirements well.
E). Cassandra is designed precisely for highly available massive datasets, and a fine tuned cluster may offer low latency in reads. Fits the requirements.
F). HDFS with Hive is great for OLAP and data-warehouse scenarios, allowing to solve map- reduce problems using an SQL subset, but the latency is usually really high (we may talk about seconds, not milliseconds, when obtaining results), so this does not complies with the requirements.


NEW QUESTION # 285
Dataproc clusters contain many configuration files. To update these files, you will need to use the
-- properties option. The format for the option is: file_prefix:property=_____.

Answer: B

Explanation:
To make updating files and properties easy, the --properties command uses a special format to specify the configuration file and the property and value within the file that should be updated.
The formatting is as follows: file_prefix:property=value.
Reference:
https://cloud.google.com/dataproc/docs/concepts/cluster-properties#formatting


NEW QUESTION # 286
Which of the following are feature engineering techniques? (Select 2 answers)

Answer: A,B

Explanation:
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_mo del


NEW QUESTION # 287
A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open.
After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

Answer: B


NEW QUESTION # 288
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

Answer: A

Explanation:
Dialogflow is used to do voice analytics on human computer interaction.


NEW QUESTION # 289
......

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