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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Streaming ingestion with Pub/Sub concepts 2. Cloud Storage ingestion patterns
Data Management	25%	- Storage and data organization 1. Cloud Storage lifecycle policies 2. BigQuery datasets and tables management - Data governance and security 1. IAM and access control 2. Data lifecycle management
Data Analysis and Presentation	27%	- Data visualization 1. Looker / Looker Studio reporting - Querying and analyzing data 1. BigQuery SQL usage 2. Data aggregation and transformation
Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines

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Google Associate Data Practitioner Sample Questions (Q63-Q68):

NEW QUESTION # 63
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A. Compare the confusion matrix.
B. Compare the two different models.
C. Evaluate data drift.
D. Evaluate the data skewness.

Answer: C

NEW QUESTION # 64
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. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
C. 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.
D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Answer: B

NEW QUESTION # 65
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A. Create a native table.
B. Create an object table.
C. Create an external table.
D. Create a temporary table.

Answer: B

NEW QUESTION # 66
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 the gcloud storage command to transfer the data over the internet.
B. Use Cloud Client Libraries to transfer the data over the internet.
C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
D. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.

Answer: C

NEW QUESTION # 67
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 create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
B. 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.
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 # 68
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