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| Section | Weight | Objectives |
|---|
| 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
|
| Data Pipeline Orchestration | 18% | - Pipeline design and automation
- 1. Workflow orchestration concepts (e.g., Cloud Composer)
- 2. Dataflow pipelines
|
| 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
|
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100% Pass ADP - Associate Data Practitioner Authoritative Training Solutions
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Google Associate Data Practitioner Sample Questions (Q16-Q21):
NEW QUESTION # 16
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 Storage Transfer Service.
- B. Use gcloud storage commands.
- C. Use a Transfer Appliance.
- D. Use Cloud Storage FUSE.
Answer: A
NEW QUESTION # 17
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. Use Cloud Scheduler to schedule the jobs to run.
- C. 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.
- D. Use Cloud Tasks to schedule and run the jobs asynchronously.
Answer: A
NEW QUESTION # 18
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
- A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
- B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- C. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
Answer: B
NEW QUESTION # 19
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 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.
- B. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
- C. 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.
- D. 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.
Answer: A
NEW QUESTION # 20
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 Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
- C. Use the PythonOperator in Cloud Composer 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.
Answer: A
NEW QUESTION # 21
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