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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q26-Q31):

NEW QUESTION # 26
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
B. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
C. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
D. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.

Answer: D

NEW QUESTION # 27
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 the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
B. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
C. 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.
D. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

Answer: B

NEW QUESTION # 28
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 Cloud Run functions 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 the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Answer: C

NEW QUESTION # 29
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
B. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Answer: C

NEW QUESTION # 30
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A. EL
B. ELT
C. ETLT
D. ETL

Answer: D

NEW QUESTION # 31
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