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| Section | Weight | Objectives |
|---|
| Data Management | 25% | - Data governance and security
- 1. Data lifecycle management
- 2. IAM and access control
- Storage and data organization
- 1. Cloud Storage lifecycle policies
- 2. BigQuery datasets and tables management
|
| Data Pipeline Orchestration | 18% | - Pipeline design and automation
- 1. Workflow orchestration concepts (e.g., Cloud Composer)
- 2. Dataflow pipelines
|
| Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
- 1. Cloud Storage ingestion patterns
- 2. Streaming ingestion with Pub/Sub concepts
|
| Data Analysis and Presentation | 27% | - Data visualization
- 1. Looker / Looker Studio reporting
- Querying and analyzing data
- 1. Data aggregation and transformation
- 2. BigQuery SQL usage
|
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Google Associate Data Practitioner Sample Questions (Q26-Q31):
NEW QUESTION # 26
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 Dataform workflows.
- B. Use Cloud Data Fusion pipelines.
- C. Use Dataflow pipelines.
- D. Use Cloud Composer operators.
Answer: A
NEW QUESTION # 27
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
- A. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
- B. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
- C. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
- D. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
Answer: C
NEW QUESTION # 28
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.
Answer: C
NEW QUESTION # 29
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?
- A. Use a Transfer Appliance.
- B. Use Storage Transfer Service.
- C. Use Cloud Storage FUSE.
- D. Use gcloud storage commands.
Answer: B
NEW QUESTION # 30
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?
- A. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
- B. Migrate the Spark jobs to Dataproc Serverless.
- C. Migrate the Spark jobs to Dataproc on Compute Engine.
- D. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
Answer: B
NEW QUESTION # 31
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