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DP-203 Design and implement data storage Practice Question

You are a data engineer for a gaming company that uses Azure Data Lake Storage Gen2. The data lake stores player event data in JSON format. The data is organized by date and event type. The analytics team frequently runs queries that filter by player ID to analyze individual player behavior. These queries are slow because they scan entire daily partitions. You need to improve the performance of queries that filter by player ID without restructuring the entire data lake. The data is stored as JSON files. What should you do?

⚠ Common exam trap

Many exam-takers think compression alone (Option A) or indexing (Option C) can solve performance issues in a data lake, but Azure Data Lake Storage Gen2 does not support file-level indexes, and compression does not change the fundamental row-scanning nature of JSON queries.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Convert the data from JSON to Parquet format and partition by player ID

Converting JSON to Parquet enables columnar storage, which significantly reduces I/O by reading only the columns needed for queries. Partitioning by player ID further improves performance by allowing partition elimination, so queries filter only the relevant partitions instead of scanning entire daily partitions. This approach directly addresses the slow queries without restructuring the entire data lake.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Compress the JSON files using gzip to reduce I/O

    Why it's wrong here

    Compression reduces size but does not enable predicate pushdown.

  • Convert the data from JSON to Parquet format and partition by player ID

    Why this is correct

    Parquet allows predicate pushdown and column pruning, speeding up player ID queries.

  • Create indexes on the player ID field in the data lake

    Why it's wrong here

    ADLS Gen2 does not support indexing on data files.

  • Repartition the data by hour to reduce the data scanned per partition

    Why it's wrong here

    Finer time partitions do not help with player ID filters.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.