ADP Exam Exercise, ADP Practice Test Pdf

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q50-Q55):

NEW QUESTION # 50
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 Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
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 # 51
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
B. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
C. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
D. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

Answer: A

NEW QUESTION # 52
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
B. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
C. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
D. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.

Answer: B

NEW QUESTION # 53
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
B. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
D. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

Answer: D

NEW QUESTION # 54
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C. Use Cloud Scheduler to schedule the jobs to run.
D. Use Cloud Tasks to schedule and run the jobs asynchronously.

Answer: A

NEW QUESTION # 55
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