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| Section | Weight | Objectives |
|---|
| Data Pipeline Orchestration | 18% | - Pipeline design and automation
- 1. Dataflow pipelines
- 2. Workflow orchestration concepts (e.g., Cloud Composer)
|
| Data Analysis and Presentation | 27% | - Querying and analyzing data
- 1. Data aggregation and transformation
- 2. BigQuery SQL usage
- Data visualization
- 1. Looker / Looker Studio reporting
|
| Data Management | 25% | - Data governance and security
- 1. IAM and access control
- 2. Data lifecycle management
- Storage and data organization
- 1. BigQuery datasets and tables management
- 2. Cloud Storage lifecycle policies
|
| Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
- 1. Cloud Storage ingestion patterns
- 2. Streaming ingestion with Pub/Sub concepts
|
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Google Associate Data Practitioner Sample Questions (Q64-Q69):
NEW QUESTION # 64
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 Cloud SQL for PostgreSQL Datastore
- B. Filestore Bigtable BigQuery
- C. Cloud Storage BigQuery Firestore
- D. Cloud Storage CloudSQL for PostgreSQL Bigtable
Answer: C
NEW QUESTION # 65
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?
- A. Evaluate the data skewness.
- B. Evaluate data drift.
- C. Compare the two different models.
- D. Compare the confusion matrix.
Answer: B
NEW QUESTION # 66
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?
- A. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
- B. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
- C. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
- D. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
Answer: D
NEW QUESTION # 67
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?
- A. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
- B. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
- C. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
- D. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
Answer: D
NEW QUESTION # 68
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?
- A. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
- B. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
- C. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
- D. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
Answer: B
NEW QUESTION # 69
......
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