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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 Pipeline Orchestration	18%	- Pipeline design and automation 1. Dataflow pipelines 2. Workflow orchestration concepts (e.g., Cloud Composer)
Topic 3: 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 4: Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Streaming ingestion with Pub/Sub concepts 2. Cloud Storage ingestion patterns

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Google Associate Data Practitioner Sample Questions (Q15-Q20):

NEW QUESTION # 15
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. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
B. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files 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: A

NEW QUESTION # 16
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 a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
B. Create BigQuery scheduled queries to define the transformations in SQL.
C. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
D. Use Dataform to define the transformations in SQLX.

Answer: D

NEW QUESTION # 17
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. 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.
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. 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.

Answer: B

NEW QUESTION # 18
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.
B. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
C. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
D. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Answer: D

NEW QUESTION # 19
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

A. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
B. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
C. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
D. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

Answer: D

NEW QUESTION # 20
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