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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation and Ingestion | 30% | - Data loading methods
|
| Data Management and Governance | 25% | - Compliance and governance
|
| Data Pipeline Orchestration | 18% | - Transformation tools selection
|
| Data Analysis and Presentation | 27% | - Business intelligence and decision support
|
Google Associate Data Practitioner Sample Questions:
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?
- A. Create a multi-region bucket, and upload the files to this bucket.
- B. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
- C. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
- D. Create a dual-region bucket, and upload the files to this bucket.
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?
- A. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
- B. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
- C. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
- D. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
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. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
- B. Create BigQuery scheduled queries to define the transformations in 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.
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 Durable Reduced Availability (DRA) 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 Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
- C. 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.
- D. 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.
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?
- A. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
- B. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
- C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
- D. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
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