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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines
Topic 2: 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
Topic 3: Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Streaming ingestion with Pub/Sub concepts 2. Cloud Storage ingestion patterns
Topic 4: Data Analysis and Presentation	27%	- Data visualization 1. Looker / Looker Studio reporting - Querying and analyzing data 1. BigQuery SQL usage 2. Data aggregation and transformation

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Google Associate Data Practitioner Sample Questions (Q14-Q19):

NEW QUESTION # 14
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Set temporary object holds.
B. Change the bucket storage class to Archive.
C. Enable Object Versioning.
D. Set a bucket retention policy.

Answer: D

NEW QUESTION # 15
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. 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.
B. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
D. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

Answer: A

NEW QUESTION # 16
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 BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
C. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
D. 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.

Answer: B

NEW QUESTION # 17
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
B. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
D. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.

Answer: A

NEW QUESTION # 18
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A. Grant each partner read access to the BigQuery dataset by using 1AM roles.
B. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
D. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

Answer: C

NEW QUESTION # 19
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