Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set4  Q61-80

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Question 61

You are designing a data pipeline in Azure Data Factory to ingest data from multiple sources into Azure Synapse Analytics. Some sources are frequently updated while others rarely change. Which approach is most efficient?

A) Use incremental loads for frequently updated sources and full loads for rarely updated sources

B) Perform full loads for all sources every day

C) Use incremental loads for all sources regardless of update frequency

D) Copy all data to a staging table before transformation

Answer: A) Use incremental loads for frequently updated sources and full loads for rarely updated sources

Explanation:

Using incremental loads for frequently updated sources ensures that only new or modified data is processed, which reduces network usage, storage costs, and computation time. This approach improves pipeline efficiency, avoids redundant processing, and ensures timely data updates in the data warehouse. Full loads are more appropriate for rarely changing datasets because the overhead of managing incremental logic may outweigh the benefits, and occasional full loads ensure completeness. Performing full loads for all sources every day consumes excessive resources, increases runtime, and can create bottlenecks in large-scale pipelines. Using incremental loads for all sources without considering update frequency can complicate the pipeline unnecessarily and may not improve efficiency for static data. Copying all data into a staging table increases storage and processing overhead, and it can create unnecessary complexity in the ETL process. By combining incremental and full load strategies, you can balance performance, scalability, and maintainability, optimizing resource usage while ensuring accurate and up-to-date analytics data. This approach follows industry best practices for hybrid ETL pipelines in Azure Data Factory, providing efficiency and reliability for diverse datasets.

Question 62

You are building a predictive model in Azure ML to forecast sales. The dataset contains missing values, categorical features, and numeric features with different scales. Which preprocessing steps are essential?

A) Handle missing values, encode categorical features, and scale numeric features

B) Drop categorical features

C) Train the model directly without preprocessing

D) Remove numeric features

Answer: A) Handle missing values, encode categorical features, and scale numeric features

Explanation:

Handling missing values ensures that the model receives complete data, avoiding errors or bias. Imputation techniques such as mean, median, or model-based methods maintain the integrity of the dataset. Encoding categorical features into numeric representations using methods like one-hot encoding, label encoding, or target encoding allows machine learning algorithms to process non-numeric data effectively. Scaling numeric features standardizes the magnitude of each feature, preventing high-scale features from dominating the model’s learning process, which is critical for algorithms like gradient descent or distance-based models. Dropping categorical features removes valuable information, reducing predictive accuracy. Training the model without preprocessing may produce poor results because the model cannot handle missing or categorical data natively, and differences in numeric scales can skew predictions. Removing numeric features eliminates essential predictors, compromising model performance. Proper preprocessing ensures that the model converges efficiently, produces accurate predictions, and provides interpretable results. These steps are fundamental to building robust, reliable predictive models in Azure ML, enabling actionable insights, such as accurate sales forecasting, while maintaining model quality and operational efficiency.

Handling missing values, encoding categorical features, and scaling numeric features are essential steps in the data preprocessing phase of machine learning. Raw datasets often contain inconsistencies, missing entries, categorical variables, and numerical features with varying ranges, all of which can negatively affect model performance if left unaddressed. Missing values can occur due to errors in data collection, integration from multiple sources, or user omissions. If not handled properly, missing data can lead to biased estimates, skewed model predictions, or algorithm failures. Common strategies for handling missing values include imputation using mean, median, or mode for numeric data, or assigning a placeholder category for categorical data. By addressing missing values systematically, the dataset becomes complete and suitable for modeling.

Encoding categorical features is equally important because most machine learning algorithms cannot interpret textual or non-numeric data directly. Categorical variables, such as product types, regions, or user segments, need to be transformed into numeric representations. Techniques like one-hot encoding create binary columns for each category, while label encoding assigns unique integers to each category. Proper encoding ensures that algorithms correctly interpret categorical distinctions and relationships without introducing unintended ordinal relationships where none exist. Failing to encode categorical features can lead to errors or poor model performance, as algorithms may treat textual data inconsistently or ignore valuable information.

Scaling numeric features is another critical preprocessing step, particularly for algorithms that rely on distance metrics, gradients, or optimization routines, such as logistic regression, support vector machines, or neural networks. Features with different ranges or magnitudes can dominate the learning process, biasing the model toward certain variables while neglecting others. Standardization (z-score scaling) or normalization (min-max scaling) brings all numeric features to a comparable scale, improving convergence during training, stabilizing learning rates, and enhancing model performance. Proper scaling also ensures that regularization techniques, such as L1 or L2 penalties, are applied evenly across features, preventing some variables from disproportionately influencing the model.

Dropping categorical features is generally a poor strategy unless the feature is irrelevant or redundant. Removing valuable categorical information reduces the dataset’s predictive power and may lead to less accurate models. Many categorical features contain important signals that are critical for prediction, and encoding them allows models to leverage these signals effectively. Dropping them indiscriminately wastes information and increases the risk of underfitting, where the model fails to capture important relationships in the data.

Training the model directly without preprocessing is also ineffective, as raw data often contains missing values, unencoded categorical features, and unscaled numeric values. Most machine learning algorithms assume complete, clean, and appropriately formatted data. Ignoring preprocessing steps can lead to errors during training, poor model generalization, biased predictions, and instability in the learning process. Skipping preprocessing undermines the foundation of the machine learning pipeline and reduces the likelihood of achieving accurate and reliable results.

Removing numeric features is equally problematic unless those features are irrelevant or redundant. Numeric variables often carry essential predictive information, and eliminating them unnecessarily diminishes the dataset’s expressiveness and reduces model accuracy. Proper preprocessing involves retaining numeric features while ensuring they are scaled appropriately, rather than discarding them.

Overall, handling missing values, encoding categorical features, and scaling numeric features is the correct approach because it ensures that the dataset is complete, consistent, and properly formatted for modeling. These steps enhance predictive performance, reduce bias, and improve the stability and reliability of the learning algorithm. Preprocessing lays the foundation for effective machine learning, making it the essential choice compared to dropping features or training models directly on raw data.

Question 63

You are designing a Power BI report that connects to multiple large datasets. Users frequently perform aggregations and drill-down analyses. Which approach optimizes performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables precompute commonly used summaries and metrics, allowing queries to retrieve results quickly without scanning millions of rows each time. This reduces query latency and improves user experience during drill-down analyses. Enabling DirectQuery avoids importing data into Power BI but results in slower performance because every visual sends queries directly to the source database, which may not be optimized for large-scale analytical queries. Removing calculated columns reduces memory usage slightly but does not resolve performance bottlenecks caused by large dataset scans. Splitting the dataset into multiple PBIX files increases management complexity and can create redundancy or inconsistencies. Aggregation tables provide a scalable, maintainable solution that balances performance and flexibility, enabling users to access precomputed metrics for faster insights while retaining the option to explore detailed data when needed. This approach is a best practice for designing high-performance Power BI reports with large datasets, ensuring fast response times, reduced resource usage, and efficient refresh operations.

Question 64

You are implementing incremental data loads from on-premises SQL Server to Azure Data Lake using Azure Data Factory. The source tables have a last-modified timestamp column. Which method is most efficient?

A) Use a watermark column to track changes and load only new or updated rows

B) Copy the entire table daily

C) Use full overwrite of existing files

D) Append all rows without considering the timestamp

Answer: A) Use a watermark column to track changes and load only new or updated rows

Explanation:

Using a watermark column allows the pipeline to identify and load only rows that are new or have been modified since the last ETL execution. This reduces network traffic, processing time, and storage usage. Copying the entire table daily consumes significant resources, increases ETL runtime, and may create unnecessary redundancy. Full overwrites of existing files are resource-intensive and increase the risk of errors or downtime during processing. Appending all rows without considering the timestamp may lead to duplicated data and inconsistencies in downstream systems. Watermark-based incremental loading is a best practice for scalable, reliable ETL pipelines, ensuring timely updates while optimizing performance. It also simplifies error handling and monitoring because only a subset of data is processed per run. This approach is ideal for large or frequently updated datasets, allowing efficient ingestion from on-premises sources to Azure storage solutions while maintaining data accuracy, consistency, and pipeline maintainability.

Question 65

You are designing column-level security in Azure SQL Database. Users need access to most columns but must not see sensitive PII data. Which feature is most appropriate?

A) Dynamic Data Masking

B) Row-Level Security

C) Transparent Data Encryption

D) Always Encrypted

Answer: A) Dynamic Data Masking

Explanation:

Dynamic Data Masking (DDM) allows sensitive columns to be hidden in query results while still permitting access to non-sensitive data. It ensures that reporting and analytics users can perform queries without viewing confidential PII. Row-Level Security controls access at the row level rather than at the column level, so it does not solve the problem of hiding specific columns. Transparent Data Encryption secures data at rest but does not prevent users from seeing sensitive information in queries. Always Encrypted protects data end-to-end but requires client-side decryption, which may complicate analytics and reporting scenarios. DDM is easy to implement, does not require application changes, and supports various masking patterns such as partial masking, randomized masking, or format-based masking. This approach balances usability and security, ensuring compliance with privacy regulations while maintaining access to non-sensitive data for analytics purposes. It is widely recommended for scenarios where users need to interact with general datasets but sensitive columns must remain concealed, providing an efficient and maintainable solution for column-level security.

Question 66

You are building an Azure ML model to predict equipment failure based on sensor data. The solution requires immediate alerts when anomalies are detected. Which deployment method should you use?

A) Azure ML Real-Time Endpoint

B) Batch Endpoint

C) Azure Data Factory Pipeline

D) Power BI Dashboard

Answer: A) Azure ML Real-Time Endpoint

Explanation:

Azure ML Real-Time Endpoints are designed to provide low-latency predictions, making them ideal for scenarios requiring immediate response, such as detecting equipment anomalies. Data can be sent continuously through REST APIs, and the model returns predictions instantly, enabling near-real-time alerts. Batch Endpoints are intended for periodic, large-scale processing and are not suitable for low-latency requirements. Azure Data Factory pipelines orchestrate ETL workflows and batch processing but cannot provide immediate predictions. Power BI dashboards are visualization tools and cannot perform predictive model scoring in real time. Real-Time Endpoints also support autoscaling, monitoring, logging, and version control, ensuring reliability and maintainability in production environments. Using this deployment approach ensures that alerts are triggered as soon as anomalies are detected, reducing downtime, preventing failures, and allowing proactive maintenance. This method integrates seamlessly with Azure IoT Hub or Event Hub for continuous data ingestion, providing a complete end-to-end solution for real-time monitoring. Overall, Real-Time Endpoints provide the responsiveness, scalability, and robustness required for mission-critical predictive maintenance applications.

Question 67

You are designing a Power BI dataset that includes several large tables. Users need to perform frequent aggregations and drill-downs. Which approach will optimize performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables store precomputed summaries of commonly used metrics, allowing queries to retrieve results quickly without scanning the entire dataset. This reduces latency and improves user experience during drill-down and aggregation operations. Enabling DirectQuery for all tables avoids importing data but can degrade performance because each visual generates live queries against the source database, which may not be optimized for analytical workloads. Removing calculated columns slightly reduces memory usage but does not address the core performance issue caused by scanning large datasets during aggregations. Splitting the dataset into multiple PBIX files increases maintenance complexity and may lead to redundancy or inconsistencies. Aggregation tables strike a balance between performance and flexibility, enabling fast access to frequently used metrics while retaining the ability to drill down into detailed data when needed. They also reduce refresh times, as only updates to source data need to be applied incrementally to the aggregated tables. This strategy aligns with best practices for high-performance Power BI datasets, ensuring fast response times, reduced resource consumption, and a scalable solution for large-scale analytics.

Question 68

You are implementing incremental data ingestion from on-premises SQL Server to Azure Data Lake. The source tables include a last-modified timestamp column. Which strategy is most efficient?

A) Use a watermark column to load only new or updated rows

B) Copy the entire table every day

C) Use full overwrite of existing files

D) Append all rows without considering timestamps

Answer: A) Use a watermark column to load only new or updated rows

Explanation:

A watermark column tracks the last processed row or timestamp, enabling the pipeline to load only new or modified data in subsequent runs. This approach reduces data transfer, computation, and storage costs while ensuring accurate and timely updates. Copying the entire table daily is resource-intensive, increases ETL runtime, and can create redundancy. Full overwrites of existing files consume unnecessary resources and may lead to downtime or errors during processing. Appending all rows without considering timestamps introduces duplicates and inconsistencies in downstream analytics. Using a watermark-based incremental load is considered best practice for scalable and efficient ETL pipelines in Azure Data Factory, ensuring that only relevant data is ingested. It simplifies monitoring and error handling because only a subset of data is processed, improving reliability. This method is particularly effective for large datasets or high-frequency update scenarios, providing a cost-efficient and maintainable approach for keeping Azure Data Lake storage synchronized with on-premises systems while supporting downstream analytics and reporting.

Question 69

You are designing a predictive model in Azure ML to forecast customer churn. The dataset contains categorical variables, numeric variables with different scales, and missing values. Which preprocessing steps are required?

A) Handle missing values, encode categorical variables, and scale numeric features

B) Drop categorical variables

C) Train the model directly without preprocessing

D) Remove numeric variables

Answer: A) Handle missing values, encode categorical variables, and scale numeric features

Explanation:

Handling missing values ensures that the model receives complete and reliable data, avoiding errors or bias. Imputation techniques such as mean, median, or model-based approaches maintain data integrity. Encoding categorical variables into numeric forms using one-hot encoding, label encoding, or target encoding allows the model to process non-numeric features effectively. Scaling numeric features ensures that all numeric variables contribute proportionally to the model, preventing high-scale features from dominating the learning process, which is critical for gradient-based or distance-based algorithms. Dropping categorical variables removes valuable predictive information, reducing model accuracy. Training without preprocessing risks poor performance due to missing values, unencoded categorical data, and inconsistent numeric scales. Removing numeric variables eliminates essential predictors, further diminishing predictive power. Proper preprocessing ensures that models converge efficiently, achieve high accuracy, and provide interpretable insights. These steps are essential for building robust, reliable predictive models in Azure ML, allowing actionable insights such as accurate customer churn prediction while maintaining operational efficiency and model reliability.

Question 70

You are building a real-time analytics solution in Azure Stream Analytics to monitor IoT sensor data. You need to compute the average sensor value over a 10-minute rolling window. Which function should you use?

A) HoppingWindow

B) TumblingWindow

C) SessionWindow

D) SnapshotWindow

Answer: A) HoppingWindow

Explanation:

Hopping windows are designed for overlapping intervals and are ideal for rolling window calculations like a 10-minute average. Each incoming event can belong to multiple overlapping windows, ensuring continuous monitoring. Tumbling windows create fixed, non-overlapping intervals, which only include each event once and do not provide continuous rolling calculations. Session windows group events based on activity separated by inactivity, suitable for session-based analysis but not fixed-time rolling computations. Snapshot windows capture the system state at specific points, which is useful for periodic reporting but cannot provide continuous rolling averages. Hopping windows enable real-time analytics with low latency, ensuring accurate monitoring and timely anomaly detection. They allow late-arriving events to be included in calculations and support continuous aggregation. This approach is widely used for IoT monitoring, real-time dashboards, and alerting systems, providing scalability, precision, and reliability. By using hopping windows, you can maintain up-to-date insights and respond quickly to sensor anomalies, which is critical for operational efficiency and decision-making in real-time environments.

Question 71

You are designing an Azure Synapse Analytics solution with a large fact table and several small dimension tables. You need to optimize join performance and reduce data movement. Which strategy should you use?

A) Hash-distribute the fact table on foreign keys and replicate small dimension tables

B) Round-robin distribute all tables

C) Replicate the fact table and hash-distribute dimension tables

D) Leave all tables unpartitioned

Answer: A) Hash-distribute the fact table on foreign keys and replicate small dimension tables

Explanation:

Hash-distributing large fact tables on foreign keys ensures that rows with the same key are colocated on the same compute node as matching dimension rows. This reduces the need for data shuffling during joins, improving query performance and reducing network overhead. Replicating small dimension tables ensures that every node has a complete copy, eliminating join-related data movement for small tables. Round-robin distribution evenly spreads data but does not align join keys, causing unnecessary data movement and slower queries. Replicating the fact table is impractical due to its large size and high storage requirements. Hash-distributing dimension tables is inefficient since small dimensions are better replicated. Leaving tables unpartitioned does not optimize join operations and may result in uneven node workloads and slower performance. The combination of hash-distributed fact tables and replicated small dimensions is a best practice in distributed data warehouse design, providing scalability, maintainability, and high-performance query execution. This approach allows for parallel processing across compute nodes and efficient resource utilization while minimizing inter-node communication, which is critical for large analytical workloads in Azure Synapse Analytics.

Question 72

You are building a predictive maintenance solution in Azure ML using streaming IoT data. The model needs to provide real-time predictions for equipment failure. Which deployment method is most suitable?

A) Azure ML Real-Time Endpoint

B) Batch Endpoint

C) Azure Data Factory Pipeline

D) Power BI Dashboard

Answer: A) Azure ML Real-Time Endpoint

Explanation:

Azure ML Real-Time Endpoints provide low-latency predictions, which is essential for scenarios where immediate responses are required, such as predictive maintenance for IoT devices. Streaming sensor data can be sent continuously via REST APIs, and the model returns instant predictions, enabling timely alerts and automated actions. Batch Endpoints are designed for processing large datasets periodically and are unsuitable for real-time low-latency scenarios. Azure Data Factory pipelines are intended for ETL processes and batch transformations, not for serving predictions. Power BI dashboards are visualization tools and cannot execute models in real-time. Real-Time Endpoints also support autoscaling, monitoring, logging, and versioning, ensuring robust and maintainable deployments in production environments. This deployment method allows rapid anomaly detection and proactive maintenance, reducing equipment downtime and operational risk. It integrates with services like Azure IoT Hub or Event Hub for seamless ingestion of streaming data. Using a Real-Time Endpoint ensures that predictive maintenance systems respond immediately to critical sensor readings, enabling operational efficiency and reliability in industrial environments.

Question 73

You are designing a Power BI dataset that includes several large tables. Users frequently perform aggregations and drill-downs. Which approach will optimize report performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables precompute commonly used summaries and metrics, allowing queries to access data quickly without scanning the entire dataset. This improves report performance for drill-down and aggregation operations, enhancing user experience. DirectQuery avoids importing data but can slow performance because every visual generates queries against the source system, which may not be optimized for analytical workloads. Removing calculated columns reduces memory usage slightly but does not resolve the core performance issue related to large dataset scans. Splitting the dataset into multiple PBIX files increases administrative overhead, creates redundancy, and may introduce inconsistencies. Aggregation tables strike a balance between performance and flexibility, enabling rapid access to frequently used metrics while retaining the ability to drill down to detailed data when needed. They also support incremental refresh, reducing the time and resources needed to update datasets. This approach follows best practices for high-performance Power BI reporting, ensuring faster response times, lower resource consumption, and a scalable solution for large datasets and complex analytics.

Question 74

You are implementing incremental data ingestion from on-premises SQL Server to Azure Data Lake using Azure Data Factory. The source tables have a last-modified timestamp column. Which strategy ensures efficient processing?

A) Use a watermark column to load only new or updated rows

B) Copy the entire table daily

C) Use full overwrite of existing files

D) Append all rows without considering timestamps

Answer: A) Use a watermark column to load only new or updated rows

Explanation:

A watermark column allows the pipeline to identify the last processed row or timestamp and ingest only newly added or modified data during subsequent runs. This reduces network traffic, storage consumption, and processing time, making ETL operations more efficient. Copying the entire table daily consumes excessive resources and can create redundancy. Full overwrite of existing files is resource-intensive and may result in downtime or errors during processing. Appending all rows without considering timestamps risks duplicates and inconsistencies in downstream systems. Using a watermark-based incremental load ensures accurate and timely ingestion while minimizing overhead. It simplifies monitoring and error handling since only the relevant subset of data is processed each run. This method is a best practice for scalable ETL pipelines in Azure Data Factory, particularly for large or frequently updated datasets. It ensures that Azure Data Lake storage remains synchronized with on-premises sources while supporting downstream analytics efficiently and reliably.

Question 75

You are designing column-level security in Azure SQL Database. Users require access to most columns but must not see sensitive PII data. Which feature is most appropriate?

A) Dynamic Data Masking

B) Row-Level Security

C) Transparent Data Encryption

D) Always Encrypted

Answer: A) Dynamic Data Masking

Explanation:

Dynamic Data Masking (DDM) hides sensitive column values in query results for non-privileged users while allowing access to non-sensitive columns. This ensures reporting and analytics users can interact with the dataset without exposing PII. Row-Level Security restricts access at the row level, not the column level, so it does not protect sensitive columns. Transparent Data Encryption secures data at rest but does not prevent sensitive information from appearing in query results. Always Encrypted provides end-to-end encryption but requires client-side decryption, which can complicate analytics and reporting workflows. DDM is simple to implement, requires no application changes, and supports various masking patterns such as partial masking, randomized masking, or custom formats. This approach balances usability and security, ensuring compliance with data privacy regulations while maintaining access to non-sensitive data. It is widely recommended for column-level security in scenarios where most information must remain visible but sensitive fields must be concealed, providing a practical and maintainable solution.

Question 76

You are designing an Azure Synapse Analytics solution with multiple large fact tables and small dimension tables. You need to optimize join performance and minimize data movement. Which strategy is most appropriate?

A) Hash-distribute the fact tables on foreign keys and replicate small dimension tables

B) Round-robin distribute all tables

C) Replicate the fact tables and hash-distribute dimension tables

D) Leave all tables unpartitioned

Answer: A) Hash-distribute the fact tables on foreign keys and replicate small dimension tables

Explanation:

Hash distribution for large fact tables ensures that rows with the same foreign key are colocated on the same compute node as matching dimension rows, reducing data shuffling during joins. This improves query performance, decreases network traffic, and allows parallel execution across nodes. Replicating small dimension tables ensures each node has a complete copy, which further minimizes inter-node data movement during joins. Round-robin distribution spreads data evenly but does not align join keys, resulting in increased data transfer and slower query execution. Replicating fact tables is not practical because they are large and would consume excessive storage and network resources. Hash-distributing dimension tables is inefficient for small tables since replication is more effective. Leaving tables unpartitioned fails to optimize joins and may lead to uneven workloads and degraded performance. Combining hash distribution for fact tables with replicated small dimensions is considered best practice for distributed data warehouse design in Azure Synapse Analytics. It provides scalability, maintainability, and high-performance query execution while reducing computational overhead and inter-node communication, which is critical for analytical workloads with frequent joins.

Question 77

You are building a predictive maintenance solution using Azure ML and streaming IoT data. The model must generate immediate alerts for potential equipment failures. Which deployment method should you choose?

A) Azure ML Real-Time Endpoint

B) Batch Endpoint

C) Azure Data Factory Pipeline

D) Power BI Dashboard

Answer: A) Azure ML Real-Time Endpoint

Explanation:

Azure ML Real-Time Endpoints deliver low-latency predictions, making them ideal for real-time decision-making, such as predictive maintenance for IoT equipment. Sensor data can be streamed continuously through REST APIs, and the model responds instantly, enabling immediate alerts and automated actions. Batch Endpoints process large datasets periodically and cannot meet low-latency requirements. Azure Data Factory pipelines handle ETL workflows and batch transformations, not real-time predictions. Power BI dashboards are visualization tools and cannot execute models for real-time scoring. Real-Time Endpoints support autoscaling, monitoring, logging, and version control, ensuring robust deployment in production. Using this deployment approach enables proactive maintenance, reduces downtime, and allows timely intervention. Integration with Azure IoT Hub or Event Hub ensures seamless ingestion of streaming data. Real-Time Endpoints provide responsiveness, scalability, and reliability, which are essential for mission-critical operational monitoring and predictive analytics.

Question 78

You are designing a Power BI dataset that includes multiple large tables. Users frequently perform aggregations and drill-downs. Which approach optimizes report performance?

A) Create aggregation tables to precompute frequently used metrics

B) Enable DirectQuery for all tables

C) Remove calculated columns

D) Split the dataset into multiple PBIX files

Answer: A) Create aggregation tables to precompute frequently used metrics

Explanation:

Aggregation tables store precomputed summaries and metrics, allowing queries to access results quickly without scanning entire datasets. This improves performance for drill-downs and aggregations, reducing latency and enhancing user experience. DirectQuery avoids importing data but can slow performance because each visual generates live queries against source databases that may not be optimized for analytical workloads. Removing calculated columns slightly reduces memory usage but does not solve performance issues caused by large dataset scans. Splitting datasets into multiple PBIX files increases administrative complexity and may lead to redundancy or inconsistencies. Aggregation tables strike a balance between speed and flexibility, enabling rapid access to commonly used metrics while still allowing detailed exploration when needed. They also support incremental refresh, reducing the processing time for large datasets. This strategy is widely recommended for high-performance Power BI reports, providing faster response times, reduced resource consumption, and a scalable solution for complex analytics scenarios.

Question 79

You are implementing incremental data ingestion from on-premises SQL Server to Azure Data Lake using Azure Data Factory. The source tables include a last-modified timestamp column. Which strategy ensures efficient processing?

A) Use a watermark column to load only new or updated rows

B) Copy the entire table daily

C) Use full overwrite of existing files

D) Append all rows without considering timestamps

Answer: A) Use a watermark column to load only new or updated rows

Explanation:

A watermark column tracks the last processed timestamp or row, enabling the pipeline to load only newly added or modified rows. This reduces network traffic, storage consumption, and processing time, making ETL operations more efficient. Copying the entire table daily is resource-intensive, increases runtime, and creates redundancy. Full overwrite of existing files consumes additional resources and may result in errors or downtime. Appending all rows without considering timestamps risks duplicates and inconsistencies in downstream systems. Using a watermark-based incremental load ensures accurate, timely ingestion while minimizing overhead. It simplifies monitoring and error handling since only relevant subsets of data are processed in each run. This approach is a best practice for scalable ETL pipelines, especially for large or frequently updated datasets. It ensures Azure Data Lake storage remains synchronized with on-premises sources while supporting downstream analytics efficiently and reliably.

Question 80

You are designing column-level security in Azure SQL Database. Users need access to most columns but must not see sensitive PII data. Which feature is most appropriate?

A) Dynamic Data Masking

B) Row-Level Security

C) Transparent Data Encryption

D) Always Encrypted

Answer: A) Dynamic Data Masking

Explanation:

Dynamic Data Masking (DDM) hides sensitive column values in query results for non-privileged users while allowing access to non-sensitive data. This ensures reporting and analytics users can perform queries without exposing PII. Row-Level Security controls access at the row level, not column level, so it cannot protect specific sensitive columns. Transparent Data Encryption secures data at rest but does not affect visibility in query results. Always Encrypted provides end-to-end encryption but requires client-side decryption, which can complicate analytics and reporting workflows. DDM is easy to implement, requires no application changes, and supports multiple masking patterns, such as partial masking, randomized masking, or format-based masking. This approach balances usability and security, ensuring compliance with data privacy regulations while maintaining access to non-sensitive columns. DDM is widely recommended for scenarios where users must interact with most data but sensitive fields must remain concealed, providing a practical, maintainable solution for column-level security.