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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Exploration	20-30%	- Explore data through visualization and queries - Identify data quality issues - Ingest and acquire data - Perform exploratory data analysis (EDA) - Transform and prepare data for analysis
Topic 2: Data Processing and Analytics	20-30%	- Apply statistical methods for analysis - Build and maintain data pipelines - Query and analyze datasets - Use BigQuery and SQL for analytics - Aggregate and summarize data
Topic 3: Data-Driven Decision Making	10-20%	- Translate business requirements into data solutions - Assess data quality and completeness - Define success metrics - Identify stakeholders and requirements
Topic 4: Data Visualization and Insights	20-30%	- Interpret and communicate findings - Build visualizations using Looker Studio - Create dashboards and reports - Present data insights to stakeholders - Choose appropriate visualization types

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

NEW QUESTION # 37
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use gcloud storage commands.
B. Use Storage Transfer Service.
C. Use Cloud Storage FUSE.
D. Use a Transfer Appliance.

Answer: B

NEW QUESTION # 38
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
B. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
C. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
D. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

Answer: C

NEW QUESTION # 39
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. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: B

NEW QUESTION # 40
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 # 41
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Answer: C

NEW QUESTION # 42
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