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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q370-Q375):

                                  NEW QUESTION # 370
                                  You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for
                                  3 years. You want to minimize storage costs. What should you do?

                                  Answer: D

                                  Explanation:
                                  This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table.
                                  Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. References:
                                  * Exporting table data | BigQuery | Google Cloud
                                  * Storage classes | Cloud Storage | Google Cloud
                                  * Retention policies and retention periods | Cloud Storage | Google Cloud
                                  * Federated queries | BigQuery | Google Cloud


                                  NEW QUESTION # 371
                                  Cloud Dataproc charges you only for what you really use with _____ billing.

                                  Answer: C

                                  Explanation:
                                  One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.
                                  Reference:
                                  https://cloud.google.com/dataproc/docs/concepts/overview


                                  NEW QUESTION # 372
                                  Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

                                  Answer: D

                                  Explanation:
                                  If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
                                  What if we don't know the set of possible values in advance?
                                  Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
                                  Reference:
                                  https://www.tensorflow.org/tutorials/wide


                                  NEW QUESTION # 373
                                  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: B


                                  NEW QUESTION # 374
                                  You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
                                  What should you do?

                                  Answer: A


                                  NEW QUESTION # 375
                                  ......

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