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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: 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
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 Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines

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Google Associate Data Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

A. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
B. Create a multi-region bucket, and upload the files to this bucket.
C. Create a dual-region bucket, and upload the files to this bucket.
D. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

Answer: C

NEW QUESTION # 50
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. 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.
B. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
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. Grant each partner read access to the BigQuery dataset by using 1AM roles.

Answer: B

NEW QUESTION # 51
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
B. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
C. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
D. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

Answer: D

NEW QUESTION # 52
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Coldline storage.
B. Store the data in Cloud Storage using Archive storage.
C. Store the data in Cloud Storage using Nearline storage.
D. Store the data in Cloud Storage using Standard storage.

Answer: C

NEW QUESTION # 53
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. 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.
B. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
C. 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.
D. 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.

Answer: C

NEW QUESTION # 54
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