Associate Data Practitioner: ADP Exam


"Associate Data Practitioner", also known as ADP exam, is a Google Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 66 Q&As to your ADP Exam preparation. In the ADP exam resources, you will cover every field and category in Google Cloud Certified Certification helping to ready you for your successful Google Certification.

  • Exam Code: ADP
  • Exam Name: Associate Data Practitioner
  • Total Questions: 66

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Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Management and Governance25%- Data quality and maintenance
  • 1. Data lifecycle management
  • 2. Data validation and cleaning
- Data security and access control
  • 1. Data encryption and protection
  • 2. IAM roles and permissions
- Compliance and governance
  • 1. Data privacy and regulatory requirements
  • 2. Data stewardship and cataloging
Topic 2: Data Pipeline Orchestration18%- Data transformation concepts
  • 1. Data transformation requirements
  • 2. ETL vs ELT processes
- Pipeline automation and scheduling
  • 1. Schedule and monitor data jobs
  • 2. Error handling and monitoring
- Transformation tools selection
  • 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
Topic 3: Data Preparation and Ingestion30%- Data extraction and transfer tools
  • 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service
  • 2. Cloud Data Fusion, Storage Transfer Service
- Data formats and classification
  • 1. Formats: CSV, JSON, Parquet, Avro, database tables
  • 2. Structured, semi-structured, unstructured data
- Storage solutions selection
  • 1. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
  • 2. Storage location types: regional, dual-regional, multi-regional, zonal
- Data loading methods
  • 1. gcloud, BQ CLI, client libraries
  • 2. Batch and streaming ingestion
Topic 4: Data Analysis and Presentation27%- Data exploration and analysis
  • 1. Identify trends, patterns, insights
  • 2. Jupyter notebooks for analysis
  • 3. BigQuery SQL queries
- Business intelligence and decision support
  • 1. Communicate findings effectively
  • 2. Answer business questions with data
- Data visualization and reporting
  • 1. Visualization best practices
  • 2. Looker dashboards and reports

Google Associate Data Practitioner Sample Questions:

You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

  • A. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
  • B. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
  • C. Grant each partner read access to the BigQuery dataset by using 1AM roles.
  • D. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
Answer: A

Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

  • A. Use Cloud Storage FUSE.
  • B. Use a Transfer Appliance.
  • C. Use gcloud storage commands.
  • D. Use Storage Transfer Service.
Answer: D

You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

  • A. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
  • B. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
  • C. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
  • D. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
Answer: A

Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

  • A. Create BigQuery scheduled queries to define the transformations in SQL.
  • B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
  • C. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
  • D. Use Dataform to define the transformations in SQLX.
Answer: D

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

  • A. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
  • B. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
  • C. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
  • D. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
Answer: A

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