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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality28%- Security and governance
  • 1. IAM and access control in GCP
    • 2. Data governance and compliance
      - Reliability and performance
      • 1. Fault tolerance and recovery strategies
        • 2. Monitoring pipelines and workloads
          Topic 2: Operationalizing machine learning models26%- Model deployment and monitoring
          • 1. Model monitoring and drift detection
            • 2. Online vs batch prediction
              - ML pipeline integration
              • 1. Vertex AI pipeline deployment
                • 2. Feature engineering and feature stores
                  Topic 3: Building and operationalizing data processing systems24%- Data processing and transformation
                  • 1. Using Dataproc, Dataflow, and BigQuery SQL
                    • 2. ETL/ELT pipeline design
                      - Data ingestion and integration
                      • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                        • 2. Streaming ingestion (Pub/Sub, Dataflow)
                          Topic 4: Designing data processing systems22%- Data architecture and storage design
                          • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                            • 2. Designing scalable and cost-effective data models
                              - Batch and streaming data processing design
                              • 1. Latency, throughput, and consistency trade-offs
                                • 2. Event-driven vs batch architectures

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q346-Q351):

                                  NEW QUESTION # 346
                                  You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

                                  Answer: D

                                  Explanation:
                                  https://cloud.google.com/compute/docs/disks/customer-managed-encryption


                                  NEW QUESTION # 347
                                  What are the minimum permissions needed for a service account used with Google Dataproc?

                                  Answer: A

                                  Explanation:
                                  Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.


                                  NEW QUESTION # 348
                                  You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

                                  Answer: C


                                  NEW QUESTION # 349
                                  You are developing a new deep learning model that predicts a customer's likelihood to buy on your ecommerce site. After running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?

                                  Answer: D

                                  Explanation:
                                  Feature selection is the one the ways to resolve overfitting. Which means reducing the features when the size of the training data is small, then the network tends to have greater control over the training data. so increasing the size of data would help.
                                  Reference:
                                  https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html


                                  NEW QUESTION # 350
                                  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: B,E,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 # 351
                                  ......

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