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Google ADP Exam Syllabus Topics:

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. Dataflow pipelines 2. Workflow orchestration concepts (e.g., Cloud Composer)
Data Management	25%	- Data governance and security 1. IAM and access control 2. Data lifecycle management - Storage and data organization 1. Cloud Storage lifecycle policies 2. BigQuery datasets and tables management

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Google Associate Data Practitioner Sample Questions (Q28-Q33):

NEW QUESTION # 28
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
B. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D. Create a Cloud Run function that runs daily and deletes files older than seven days.

Answer: B

NEW QUESTION # 29
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
B. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
C. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
D. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.

Answer: D

NEW QUESTION # 30
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
B. Use BigQuery ML to create a logistic regression model for purchase prediction.
C. Use Colab Enterprise to develop a custom model for purchase prediction.
D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Answer: B

NEW QUESTION # 31
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.

Answer: A

NEW QUESTION # 32
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. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
C. 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.
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: A

NEW QUESTION # 33
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