Exam ADP Practice | ADP Exam Flashcards

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| Section | Weight | Objectives |
|---|
| Topic 1: Data Analysis and Presentation | 27% | - Querying and analyzing data
- 1. BigQuery SQL usage
- 2. Data aggregation and transformation
- Data visualization
- 1. Looker / Looker Studio reporting
|
| Topic 2: Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
- 1. Streaming ingestion with Pub/Sub concepts
- 2. Cloud Storage ingestion patterns
|
| Topic 3: Data Management | 25% | - Storage and data organization
- 1. Cloud Storage lifecycle policies
- 2. BigQuery datasets and tables management
- Data governance and security
- 1. IAM and access control
- 2. Data lifecycle management
|
| Topic 4: Data Pipeline Orchestration | 18% | - Pipeline design and automation
- 1. Workflow orchestration concepts (e.g., Cloud Composer)
- 2. Dataflow pipelines
|
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Google Associate Data Practitioner Sample Questions (Q15-Q20):
NEW QUESTION # 15
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?
- A. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
- B. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
- C. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
- D. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
Answer: D
NEW QUESTION # 16
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?
- A. Use Dataflow pipelines.
- B. Use Dataform workflows.
- C. Use Cloud Data Fusion pipelines.
- D. Use Cloud Composer operators.
Answer: B
NEW QUESTION # 17
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?
- A. Store the data in Cloud Storage using Nearline storage.
- B. Store the data in Cloud Storage using Standard storage.
- C. Store the data in Cloud Storage using Coldline storage.
- D. Store the data in Cloud Storage using Archive storage.
Answer: A
NEW QUESTION # 18
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
- A. Use Cloud Run functions to trigger the Spark job and email the report.
- B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- C. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
- D. Use Cloud Composer to orchestrate the Spark job and email the report.
Answer: C
NEW QUESTION # 19
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?
- A. Use Dataflow to transform the data and write the transformed data to BigQuery.
- B. Use Cloud Run functions to transform and load the data into BigQuery.
- C. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
- D. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
Answer: A
NEW QUESTION # 20
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