ADP - Associate Data Practitioner Authoritative Test Answers

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q22-Q27):

NEW QUESTION # 22
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 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.
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 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.
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 # 23
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 BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
C. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
D. 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.

Answer: A

NEW QUESTION # 24
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
B. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
C. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
D. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

Answer: C

NEW QUESTION # 25
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
B. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
C. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
D. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

Answer: D

NEW QUESTION # 26
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A. Use Dataproc to run MapReduce jobs for distributed data processing.
B. Use Cloud Data Fusion to visually design and manage the pipelines.
C. Use Dataform to build, orchestrate, and monitor the pipelines.
D. Use Cloud Composer to orchestrate and run data workflows.

Answer: C

NEW QUESTION # 27
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