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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q60-Q65):

NEW QUESTION # 60
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Dataform workflows.
B. Use Dataflow pipelines.
C. Use Cloud Data Fusion pipelines.
D. Use Cloud Composer operators.

Answer: A

NEW QUESTION # 61
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
B. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.

Answer: B

NEW QUESTION # 62
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
B. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
C. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
D. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.

Answer: D

NEW QUESTION # 63
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 Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
B. 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.
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 # 64
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
B. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
C. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
D. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.

Answer: B

NEW QUESTION # 65
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