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Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Management25%- Data governance and security
  • 1. Data lifecycle management
    • 2. IAM and access control
      - Storage and data organization
      • 1. Cloud Storage lifecycle policies
        • 2. BigQuery datasets and tables management
          Topic 2: Data Pipeline Orchestration18%- Pipeline design and automation
          • 1. Workflow orchestration concepts (e.g., Cloud Composer)
            • 2. Dataflow pipelines
              Topic 3: Data Analysis and Presentation27%- Data visualization
              • 1. Looker / Looker Studio reporting
                - Querying and analyzing data
                • 1. BigQuery SQL usage
                  • 2. Data aggregation and transformation
                    Topic 4: Data Preparation and Ingestion30%- Data ingestion into Google Cloud services
                    • 1. Cloud Storage ingestion patterns
                      • 2. Streaming ingestion with Pub/Sub concepts

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                        Google Associate Data Practitioner Sample Questions (Q26-Q31):

                        NEW QUESTION # 26
                        Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

                        Answer: C


                        NEW QUESTION # 27
                        You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

                        Answer: D


                        NEW QUESTION # 28
                        You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

                        Answer: D


                        NEW QUESTION # 29
                        Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

                        Answer: C


                        NEW QUESTION # 30
                        You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
                        The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
                        The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

                        Answer: A


                        NEW QUESTION # 31
                        ......

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