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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data-Driven Decision Making	10-20%	- Translate business requirements into data solutions - Define success metrics - Identify stakeholders and requirements - Assess data quality and completeness
Data Processing and Analytics	20-30%	- Query and analyze datasets - Aggregate and summarize data - Build and maintain data pipelines - Use BigQuery and SQL for analytics - Apply statistical methods for analysis
Data Preparation and Exploration	20-30%	- Perform exploratory data analysis (EDA) - Explore data through visualization and queries - Ingest and acquire data - Transform and prepare data for analysis - Identify data quality issues
Data Visualization and Insights	20-30%	- Choose appropriate visualization types - Build visualizations using Looker Studio - Present data insights to stakeholders - Create dashboards and reports - Interpret and communicate findings

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Google Associate Data Practitioner Sample Questions (Q37-Q42):

NEW QUESTION # 37
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. 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.
B. 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.
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 Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.

Answer: A

NEW QUESTION # 38
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 multi-region bucket, and upload the files to this bucket.
B. 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.
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 # 39
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 Compute Engine.
B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
C. Migrate the Spark jobs to Dataproc Serverless.
D. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

Answer: C

NEW QUESTION # 40
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 BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
B. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
D. Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Answer: D

NEW QUESTION # 41
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B. Use Cloud Run functions to trigger the Spark job and email the report.
C. Use Cloud Composer to orchestrate the Spark job and email the report.
D. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Answer: D

NEW QUESTION # 42
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