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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Management | 25% | - Data governance and security
|
| Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
|
| Data Pipeline Orchestration | 18% | - Pipeline design and automation
|
| Data Analysis and Presentation | 27% | - Data visualization
|
Google Associate Data Practitioner Sample Questions:
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?
- A. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
- B. Use the gcloud storage command to transfer the data over the internet.
- C. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
- D. Use Cloud Client Libraries to transfer the data over the internet.
Correct Answer: A 🗳️
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?
- A. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
- B. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
- C. Grant the partners the bigquery.user IAM role on the BigQuery project.
- D. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
Correct Answer: B 🗳️
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?
- A. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- B. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- C. Use Cloud Scheduler to schedule the jobs to run.
- D. Use Cloud Tasks to schedule and run the jobs asynchronously.
Correct Answer: A 🗳️
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?
- A. Require users to specify a partition filter using the alter table statement in SQL.
- B. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
- C. Create a view that filters out rows that are older than one year.
- D. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
Correct Answer: B 🗳️
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?
- A. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
- B. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
- C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
- D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
Correct Answer: C 🗳️
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