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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q62-Q67):

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 materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
B. 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.
C. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
D. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.

Answer: A

NEW QUESTION # 63
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Create BigQuery scheduled queries to define the transformations in SQL.
B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
C. Use Dataform to define the transformations in SQLX.
D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Answer: C

NEW QUESTION # 64
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
B. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
C. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
D. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

Answer: B

NEW QUESTION # 65
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
B. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
C. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
D. Store all data in a single BigQuery table without partitioning or lifecycle policies.

Answer: A

NEW QUESTION # 66
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. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
B. 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.
C. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
D. 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.

Answer: A

NEW QUESTION # 67
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