Exam Name: Associate Data Practitioner
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation and Ingestion | 30% | - Storage solutions selection
|
| Topic 2: Data Analysis and Presentation | 27% | - Data exploration and analysis
|
| Topic 3: Data Management and Governance | 25% | - Data security and access control
|
| Topic 4: Data Pipeline Orchestration | 18% | - Data transformation concepts
|
Google Associate Data Practitioner Sample Questions:
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?
- A. Filestore Bigtable BigQuery
- B. Filestore Cloud SQL for PostgreSQL Datastore
- C. Cloud Storage CloudSQL for PostgreSQL Bigtable
- D. Cloud Storage BigQuery Firestore
Correct Answer: D 🗳️
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 BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
- B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
- C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for 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.
Correct Answer: A 🗳️
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?
- A. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
- B. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
- C. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
- D. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
Correct Answer: D 🗳️
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?
- A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
- B. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
- C. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
- D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
Correct Answer: A 🗳️
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?
- A. Dataflow BigQuery
- B. BigQuery Analytics Hub
- C. Dataproc Serverless Bigtable
- D. Cloud Composer Cloud SQL for MySQL
Correct Answer: A 🗳️
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