Testing GCP-DE Center & Sample GCP-DE Questions Pdf

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems30%- Data pipeline implementation
  • 1. Dataproc and Spark-based processing
    • 2. Dataflow pipeline development
      - Data ingestion and transformation
      • 1. ETL/ELT workflows
        • 2. Pub/Sub streaming ingestion
          Designing data processing systems20%- Storage and data modeling
          • 1. Data lake architecture on Google Cloud Storage
            • 2. Data warehouse design using BigQuery
              - Data pipeline architecture design
              • 1. Scalable data ingestion design
                • 2. Batch vs streaming data processing selection
                  Maintaining and optimizing data and ML solutions20%- Security and governance
                  • 1. Data encryption and compliance
                    • 2. IAM and access control
                      - Machine learning integration
                      • 1. BigQuery ML usage
                        • 2. Vertex AI integration for pipelines
                          Operationalizing data and ML pipelines30%- Monitoring and troubleshooting
                          • 1. Logging and observability
                            • 2. Performance optimization and debugging
                              - Pipeline automation and orchestration
                              • 1. Scheduling and monitoring pipelines
                                • 2. Cloud Composer workflows

                                  >> Testing GCP-DE Center <<

                                  Sample GCP-DE Questions Pdf & GCP-DE Valid Test Tips

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                                  Google Data Engineer Sample Questions (Q44-Q49):

                                  NEW QUESTION # 44
                                  Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

                                  Answer: A,B

                                  Explanation:
                                  using a timestamp as the first element of a row key can cause a variety of problems.
                                  In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
                                  Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
                                  https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


                                  NEW QUESTION # 45
                                  Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
                                  You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

                                  Answer: A,B


                                  NEW QUESTION # 46
                                  You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

                                  Answer: B,J


                                  NEW QUESTION # 47
                                  You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

                                  Answer: C


                                  NEW QUESTION # 48
                                  For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

                                  Answer: C

                                  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 # 49
                                  ......

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