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Google ADP Exam Syllabus Topics:

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

>> New ADP Exam Question <<

Associate Data Practitioner Actual Exam & ADP Practice Vce & Associate Data Practitioner Updated Torrent

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Google Associate Data Practitioner Sample Questions (Q36-Q41):

NEW QUESTION # 36
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 # 37
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 create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
C. 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.
D. 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.

Answer: C

NEW QUESTION # 38
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A. Use Cloud Client Libraries to transfer the data over the internet.
B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C. Use the gcloud storage command to transfer the data over the internet.
D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Answer: D

NEW QUESTION # 39
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 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.
B. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
C. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
D. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.

Answer: C

NEW QUESTION # 40
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 Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C. Use Cloud Tasks to schedule and run the jobs asynchronously.
D. Use Cloud Scheduler to schedule the jobs to run.

Answer: B

NEW QUESTION # 41
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